{"id":1796,"date":"2026-07-29T04:52:32","date_gmt":"2026-07-29T11:52:32","guid":{"rendered":"https:\/\/www.five.reviews\/?p=1796"},"modified":"2026-07-29T04:52:33","modified_gmt":"2026-07-29T11:52:33","slug":"a2a-vs-mcp-protocol","status":"publish","type":"post","link":"https:\/\/www.five.reviews\/ai-tools\/a2a-vs-mcp-protocol\/","title":{"rendered":"A2A vs MCP: Understanding the Key Differences (2026 Guide)"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">When comparing <strong>A2A vs MCP<\/strong>, it&#8217;s easy to assume they&#8217;re competing protocols for AI agents. In reality, they solve different problems. As AI applications evolve from simple chatbots to autonomous agents, developers face two key challenges: <strong>How do agents securely access tools, APIs, and databases?<\/strong> And <strong>how do multiple agents communicate and collaborate efficiently?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That&#8217;s where <strong>Model Context Protocol (MCP)<\/strong> and <strong>Agent-to-Agent (A2A)<\/strong> come in. MCP standardises how an AI agent connects to external tools, data sources, and services through a unified interface. A2A standardises how autonomous agents discover one another, delegate tasks, exchange information, and collaborate across distributed systems. One enables <strong>agent-to-tool communication<\/strong>; the other enables <strong>agent-to-agent collaboration<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Understanding <strong>A2A vs MCP<\/strong> is essential for designing scalable and secure AI systems. In this guide, you&#8217;ll learn how each protocol works, their key differences, when to use one or both, and how they fit into modern enterprise AI architectures.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Quick Recommendations<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Use MCP if a single agent (or team of tightly coupled agents) needs standardized access to tools, databases, APIs, and data sources.<\/li>\n\n\n\n<li>Use A2A when multiple independent agents need to discover each other, delegate work, and maintain separate identities and state.<\/li>\n\n\n\n<li>Use both together for enterprise systems where agents access tools through MCP and coordinate through A2A.<\/li>\n\n\n\n<li>MCP is production-ready and adopted by 97+ million downloads. A2A reached v1.0 in March 2026 and is growing rapidly in enterprise deployments.<\/li>\n\n\n\n<li>Neither replaces the other. They solve problems at different architectural layers.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>MCP vs A2A: Comparison Table<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Aspect<\/strong><\/td><td><strong>MCP<\/strong><\/td><td><strong>A2A<\/strong><\/td><\/tr><tr><td><strong>Creator<\/strong><\/td><td>Anthropic (November 2024)<\/td><td>Google Cloud (April 2025), now Linux Foundation<\/td><\/tr><tr><td><strong>Problem Solved<\/strong><\/td><td>Unified tool and data access<\/td><td>Agent-to-agent collaboration<\/td><\/tr><tr><td><strong>Architecture Layer<\/strong><\/td><td>Vertical (agent to capabilities)<\/td><td>Horizontal (agent to agent)<\/td><\/tr><tr><td><strong>Communication Model<\/strong><\/td><td>Request-response (agent asks for tool)<\/td><td>Task delegation (agent delegates to peer)<\/td><\/tr><tr><td><strong>Agent Discovery<\/strong><\/td><td>Manual configuration or registry<\/td><td>Agent Cards at \/.well-known\/agent-card.json<\/td><\/tr><tr><td><strong>Tool Access<\/strong><\/td><td>Tools, resources, prompts<\/td><td>None (uses MCP for capability access)<\/td><\/tr><tr><td><strong>State Management<\/strong><\/td><td>Stateless tool calls<\/td><td>Stateful task lifecycle with 7 states<\/td><\/tr><tr><td><strong>Streaming<\/strong><\/td><td>Streaming resources supported<\/td><td>SSE streaming for real-time updates<\/td><\/tr><tr><td><strong>Long-running Tasks<\/strong><\/td><td>Limited<\/td><td>Built-in support with progress tracking<\/td><\/tr><tr><td><strong>Authentication<\/strong><\/td><td>API keys, OAuth 2.0, HTTP auth<\/td><td>API keys, OAuth 2.0, mutual TLS<\/td><\/tr><tr><td><strong>Transport<\/strong><\/td><td>JSON-RPC over stdio, HTTP, SSE<\/td><td>JSON-RPC over HTTP, gRPC<\/td><\/tr><tr><td><strong>Scalability<\/strong><\/td><td>1:many (one agent to many tools)<\/td><td>Many:many (distributed agents)<\/td><\/tr><tr><td><strong>Typical Complexity<\/strong><\/td><td>2-4 weeks to production MVP<\/td><td>4-8 weeks to production MVP<\/td><\/tr><tr><td><strong>Best Use Case<\/strong><\/td><td>IDE assistants, CRM access, GitHub<\/td><td>Enterprise AI assistants, research teams<\/td><\/tr><tr><td><strong>Ecosystem Maturity<\/strong><\/td><td>Highly mature, 500+ servers<\/td><td>Growing, 150+ organizations supporting<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is MCP?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The Model Context Protocol emerged from a practical frustration. Before MCP, every time you wanted to connect an AI model to a new data source or tool, you had to write custom integration code. Adding a GitHub connection meant custom code. Adding Slack meant more custom code. Adding your database meant even more. This created what Anthropic called the M\u00d7N problem: M models multiplied by N tools equals M\u00d7N separate integrations to maintain.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MCP solves this by creating a universal adapter layer. It&#8217;s like USB-C for AI. Any host that speaks MCP can connect to any MCP server and immediately discover and use its capabilities without custom code.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MCP Architecture<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">MCP operates through a three-layer model:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The Host<\/strong> is your AI application. Claude Desktop, Cursor, VS Code with Copilot, or any LLM-powered tool that implements the MCP client. The host orchestrates which MCP servers to connect to and coordinates tool calls.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The Client<\/strong> handles the dedicated connection to a single MCP server. Each server gets one client. The client speaks JSON-RPC 2.0 over a transport layer (stdio for local servers, HTTP plus Server-Sent Events for remote servers). The client discovers what tools, resources, and prompts the server exposes and manages authentication.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The Server<\/strong> is the capability provider. It exposes three core primitives:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Tools are executable functions the agent can call (search the web, write files, query a database)<\/li>\n\n\n\n<li>Resources are readable data sources (file contents, database records, API responses)<\/li>\n\n\n\n<li>Prompts are predefined templates with dynamic parameters (system prompts for specialized agents)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Communication Flow<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When you ask Claude &#8220;What are our Q3 results in Salesforce?&#8221;, here&#8217;s what happens:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Claude recognizes it needs to query Salesforce<\/li>\n\n\n\n<li>Claude client sends a JSON-RPC request to the Salesforce MCP server<\/li>\n\n\n\n<li>The server processes the request and returns the tool result<\/li>\n\n\n\n<li>Claude incorporates the data into its reasoning and responds to you<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">There&#8217;s no custom glue code. Both the client and server speak the same language.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Real-World MCP Servers<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">By mid-2026, the ecosystem included production MCP servers for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Filesystem (read\/write local files)<\/li>\n\n\n\n<li>GitHub (search repositories, create pull requests, comment on issues)<\/li>\n\n\n\n<li>PostgreSQL (query databases directly)<\/li>\n\n\n\n<li>Brave Search (web search)<\/li>\n\n\n\n<li>Slack (read channels, post messages)<\/li>\n\n\n\n<li>Figma (access design systems)<\/li>\n\n\n\n<li>Stripe (payment operations)<\/li>\n\n\n\n<li>Over 200 community-maintained servers<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What Is A2A?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A2A tackles a different problem. MCP assumes a single agent (or tightly coupled agents) calling passive tools. But modern enterprises run dozens or hundreds of independent agents spanning finance, HR, operations, supply chain, and customer service. These agents need to discover each other, delegate complex work, maintain separate state and authentication, and collaborate without constant human oversight.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A2A (Agent-to-Agent Protocol) is Google&#8217;s answer to multi-agent coordination. Launched in April 2025 and donated to the Linux Foundation in December 2025, A2A provides a standardized way for autonomous agents to communicate at scale.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>A2A Architecture<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Unlike MCP&#8217;s vertical model, A2A operates horizontally between independent agents.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Agent Cards<\/strong> are the foundation. Every A2A-compliant agent publishes a JSON document at \/.well-known\/agent-card.json describing itself:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What it does (human-readable description)<\/li>\n\n\n\n<li>What tasks it handles (specialized capabilities)<\/li>\n\n\n\n<li>How to authenticate and contact it<\/li>\n\n\n\n<li>What protocols and formats it supports<\/li>\n\n\n\n<li>Service level agreements and rate limits<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Task Delegation<\/strong> is how agents work together. An orchestrator agent breaks a complex request into sub-tasks and delegates each to the most capable peer agent, regardless of vendor:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A Google Workspace agent delegates document analysis to a specialized LLM on Azure<\/li>\n\n\n\n<li>A Salesforce CRM agent routes a support escalation to a ServiceNow ITSM agent<\/li>\n\n\n\n<li>A finance agent hands off expense processing to a compliance-specialized agent<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Task State Management<\/strong> tracks work as it moves between agents. A2A defines seven task states:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Submitted (received by remote agent)<\/li>\n\n\n\n<li>Working (actively processing)<\/li>\n\n\n\n<li>Input-required (agent needs clarification)<\/li>\n\n\n\n<li>Blocked (waiting on external resource)<\/li>\n\n\n\n<li>Completed (finished successfully)<\/li>\n\n\n\n<li>Failed (execution error)<\/li>\n\n\n\n<li>Cancelled (manually stopped)<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Each state includes timestamps and progress updates via Server-Sent Events (SSE), so the requesting agent gets real-time visibility.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Communication Mechanics<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A2A uses JSON-RPC 2.0 on top of HTTP, gRPC, or long-polling transports. When one agent delegates a task to another:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Requesting agent discovers the peer agent via its Agent Card<\/li>\n\n\n\n<li>Requesting agent sends a CreateTask message with the objective and parameters<\/li>\n\n\n\n<li>Remote agent accepts and begins processing asynchronously<\/li>\n\n\n\n<li>Remote agent sends status updates via SSE streaming<\/li>\n\n\n\n<li>Requesting agent polls for completion or receives push notifications<\/li>\n\n\n\n<li>Remote agent returns artifacts (results, files, structured data)<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">The key difference from MCP: the remote agent has full autonomy over execution. It&#8217;s not a passive tool. It reasons about how to complete the task, makes its own decisions, and maintains its own state.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How MCP Works: Real Example<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Let&#8217;s say you&#8217;re building an AI research assistant that needs to access GitHub repositories, search the web, and query your internal PostgreSQL database. Here&#8217;s the MCP workflow:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Setup Phase<\/strong><\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Deploy three MCP servers: GitHub server, Brave Search server, PostgreSQL server<\/li>\n\n\n\n<li>Configure Claude Desktop to connect to all three servers<\/li>\n\n\n\n<li>Each server exposes its available tools and resources<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Query Phase<\/strong><strong><br><\/strong> User: &#8220;Find open issues in our monorepo related to authentication and summarize the current status.&#8221;<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Claude processes the request and determines it needs GitHub access<\/li>\n\n\n\n<li>Claude calls the GitHub MCP server&#8217;s &#8220;search_issues&#8221; tool with query parameters<\/li>\n\n\n\n<li>GitHub server returns matching issues (actor: GitHub API, not Claude)<\/li>\n\n\n\n<li>Claude next needs context from the database<\/li>\n\n\n\n<li>Claude calls the PostgreSQL server&#8217;s &#8220;query_database&#8221; tool<\/li>\n\n\n\n<li>Database server executes a query and returns structured data<\/li>\n\n\n\n<li>Claude synthesizes both data sources and provides a summary<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Each step is standardized. No custom integration between Claude and GitHub. No custom integration between Claude and your database. The protocol handles the handshake.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How A2A Works: Multi-Agent Example<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine a financial services company running multiple AI agents:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Expense Manager Agent<\/strong> \u2013 Handles employee reimbursements<\/li>\n\n\n\n<li><strong>Compliance Agent<\/strong> \u2013 Validates expenses against company policies and regulations<\/li>\n\n\n\n<li><strong>Finance Agent<\/strong> \u2013 Processes approved payments<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">An expense report exceeds the normal approval threshold. Here&#8217;s how A2A handles it:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Discovery Phase<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Expense Manager discovers available agents by reading their published <strong>Agent Cards<\/strong> and identifies the <strong>Compliance Agent<\/strong> as the right agent to review the expense.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Task Delegation Phase<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Expense Manager creates a task:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>&#8220;Review expense report XYZ against company policy.&#8221;<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It sends the request to the Compliance Agent using A2A. The Compliance Agent accepts the task, moves to the <strong>Working<\/strong> state, and streams progress updates such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Checking travel policy<\/li>\n\n\n\n<li>Validating receipt amounts<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>State Management Phase<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Expense Manager subscribes to real-time updates via Server-Sent Events (SSE):<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>14:32<\/strong> &#8211; <em>Input required: Clarify business purpose.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>14:35<\/strong> &#8211; Expense Manager provides the clarification.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>14:38<\/strong> &#8211; <em>Completed-Approved with exceptions.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The Compliance Agent returns the approval decision, notes, and the updated expense record.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Handoff Phase<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The Expense Manager creates a new task for the <strong>Finance Agent<\/strong> to process the approved payment. The Finance Agent completes the payment independently while the Expense Manager continues coordinating the overall workflow.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Each agent maintains its own state, reasoning, and operational boundaries. That&#8217;s what makes <strong>A2A<\/strong> ideal for coordinating autonomous agents across complex workflows.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A2A vs MCP: Key Differences Explained<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>The Vertical vs Horizontal Distinction<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The most fundamental difference between these protocols is architectural direction:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MCP is vertical. An agent sits at the top and calls down to passive tools and data sources. The agent maintains control of the workflow. The tool responds to requests but doesn&#8217;t reason or plan. You might have multiple tools stacked vertically, but the agent orchestrates all of them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A2A is horizontal. Agents sit beside each other as peers. Each maintains its own reasoning, planning, and execution. They communicate laterally to delegate work and share status. No single agent controls the multi-agent system. Orchestration emerges from task delegation patterns.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Control vs Autonomy<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In MCP workflows, the calling agent retains control. If it calls a database tool to fetch records, it knows exactly what the tool will do. The tool executes the request and returns results. The agent decides what to do next.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In A2A workflows, the requesting agent delegates control to the remote agent. It specifies the objective but not the method. The remote agent decides how to accomplish the task, what questions to ask, what data to gather, and how to iterate. Control is intentionally surrendered.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Scalability Patterns<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">MCP scales through breadth. One agent with many tools. Claude connected to 10 different MCP servers is one vertical stack scaled horizontally across capabilities. This works well for single-agent scenarios or tightly coordinated teams.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A2A scales through distribution. Many agents with their own capabilities. Hundreds of independent agents discovering each other and forming task networks. This works for enterprise systems where you can&#8217;t predict in advance which agents will need to collaborate.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Governance and Security<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">MCP puts governance at the host level. The Claude Desktop administrator controls which servers can connect and what permissions they have. It&#8217;s centralized decision-making suitable for personal tools or departmental systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A2A distributes governance. Each agent publishes its own Agent Card with its own authentication requirements. Access control happens at the agent boundary. This is necessary in federated enterprises where different departments own different agents.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Enterprise Adoption<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As of mid-2026, MCP is far more widely deployed. Over 97 million downloads. Native support in Claude, ChatGPT, Cursor, VS Code, Zed, and dozens of other tools. Most enterprises have MCP servers running in production today.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A2A reached v1.0 in March 2026 with 150+ organizations supporting the protocol. Production adoption is growing but still concentrated among early adopters and enterprises building large-scale multi-agent systems. By 2027, expect rapid adoption as enterprises move from single-agent to multi-agent architectures.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Can A2A and MCP Work Together?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Yes, absolutely. They work at different layers and complement each other perfectly.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The complementary architecture looks like this:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Orchestrator Agent<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&nbsp;&nbsp;&nbsp;&nbsp;\u2193 (delegates via A2A)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Research Agent (with MCP clients)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&nbsp;&nbsp;&nbsp;&nbsp;\u2193 (tool calls via MCP)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">GitHub Server | Slack Server | Database Server<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Here&#8217;s how it works in practice:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Layer 1: Agent Access (MCP)<\/strong><strong><br><\/strong> Each agent internally uses MCP to access tools and data. A Research Agent connects to GitHub, web search, and internal documentation via MCP servers. A Finance Agent connects to accounting systems via MCP.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Layer 2: Agent Coordination (A2A)<\/strong><strong><br><\/strong> An Orchestrator Agent delegates tasks to the Research Agent and Finance Agent via A2A. It doesn&#8217;t care how they access their tools. It only cares that they complete the objective.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Layer 3: Results<br><\/strong> Research Agent returns its findings via A2A. Finance Agent returns budget data via A2A. Orchestrator Agent synthesizes results and presents them to the user.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is the standard enterprise AI architecture emerging in 2026. Every serious multi-agent system uses REST for human-API communication, MCP for agent-to-capability communication, and A2A for agent-to-agent coordination.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><em>Read More:<a href=\"https:\/\/www.five.reviews\/ai-tools\/best-mcp-servers-for-ai-agents\/\" target=\"_blank\" rel=\"noreferrer noopener\"> 10 Best MCP Servers for AI Agents in 2026 (Free &amp; Paid)<\/a><\/em><\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Real-World Use Cases<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MCP: When to Use<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>IDE and Code Editing<\/strong><strong><br><\/strong> Cursor and VS Code with Copilot use MCP to connect AI assistants to the filesystem, terminal, git history, linter output, and test runners. The assistant accesses all these capabilities through a single protocol. When you ask Cursor to &#8220;refactor this function&#8221;, it uses MCP tools to read the file, run tests, and suggest changes.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Customer Support<\/strong><strong><br><\/strong> A support AI agent uses MCP to access a CRM (Salesforce), ticketing system (Zendesk), knowledge base (Confluence), and customer communication history. When a customer question comes in, the agent queries all these sources through MCP and provides a comprehensive, personalized response.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Business Intelligence<\/strong><strong><br><\/strong> An analytics agent uses MCP to query multiple databases, access data warehouses, pull from APIs, and generate reports. Instead of building custom connectors for each data source, all connections use MCP.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Coding Agents<\/strong><strong><br><\/strong> GitHub Copilot uses MCP to access repository context, run tests, and pull files. The agent can read your codebase, understand the context, and suggest improvements all through MCP.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>A2A: When to Use<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Enterprise AI Assistants<\/strong><strong><br><\/strong> A large financial services company runs specialized agents: Portfolio Manager Agent, Risk Assessment Agent, Compliance Agent, and Execution Agent. When a customer request comes in, the Portfolio Manager delegates risk analysis to Risk Assessment, gets compliance approval from Compliance Agent, and coordinates execution through the Execution Agent. Each agent maintains independent state and expertise.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Research Teams<br><\/strong> A research organization has a Literature Review Agent (accesses academic databases), an Experiment Planning Agent (designs research protocols), and a Results Analysis Agent (interprets data). These agents collaborate on multi-month research projects, each delegating tasks as needed. The Literature Review Agent hands off findings to Experiment Planning. Experiment Planning delegates data analysis to Results Analysis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Travel Planning Systems<\/strong><strong><br><\/strong> An orchestrator agent receives a travel request and delegates tasks: Flight Booking Agent finds flights, Hotel Agent finds accommodation, Itinerary Agent builds the schedule, and Visa Agent checks requirements. Each agent operates independently with its own data and expertise.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Supply Chain Automation<\/strong><strong><br><\/strong> A Procurement Agent receives a purchase order and delegates supplier evaluation to Vendor Evaluation Agent, regulatory checks to Compliance Agent, budget approval to Finance Agent, and logistics coordination to Shipping Agent. Tasks flow through the network asynchronously.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Decision Framework: Which Protocol Do You Need?<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Choosing between MCP and A2A is simpler than it might seem. It all comes down to <strong>what your AI system needs to do<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Start with MCP if.<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>You&#8217;re building a <strong>single AI agent<\/strong> that needs access to tools, APIs, databases, or internal systems.<\/li>\n\n\n\n<li>One agent is responsible for planning and execution, while external tools simply perform requested actions.<\/li>\n\n\n\n<li>You want a standard way to integrate capabilities without maintaining custom connectors.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For most AI applications today, <strong>MCP is all you need<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Consider A2A if.<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Your application uses <strong>multiple independent AI agents<\/strong> with distinct responsibilities.<\/li>\n\n\n\n<li>Agents need to discover one another, delegate tasks, and exchange updates while maintaining their own state and decision-making.<\/li>\n\n\n\n<li>You&#8217;re starting to build custom orchestration or routing logic between agents.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">If that sounds familiar, it&#8217;s a strong sign that <strong>A2A is the next step<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>When Should You Use Both?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The best enterprise AI systems don&#8217;t choose between MCP and A2A-they combine them.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Single agent with tool access:<\/strong> MCP only<\/li>\n\n\n\n<li><strong>Multiple independent agents:<\/strong> A2A for coordination<\/li>\n\n\n\n<li><strong>Multi-agent systems with tool access:<\/strong> MCP for tools + A2A for agent communication<\/li>\n\n\n\n<li><strong>Enterprise-scale AI platforms:<\/strong> MCP and A2A together as the standard architecture<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Common Architecture Patterns<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Pattern 1: Single Agent + MCP<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Claude Desktop \u2194 MCP Servers (GitHub, Slack, PostgreSQL)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> Personal productivity, coding assistants, and small teams.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Pattern 2: Orchestrator + Specialized Agents<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Orchestrator Agent (A2A) \u2192 Specialized Agents (each using MCP)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> Growing organisations with multiple AI workflows and clear task delegation.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Pattern 3: Enterprise Multi-Agent Platform<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Dozens of specialised agents communicate through A2A while each agent accesses tools and services through MCP.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Best for:<\/strong> Large enterprises running complex, distributed AI systems across multiple departments.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Best Practices for Implementation<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MCP Implementation<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Start with One Server<\/strong><strong><br><\/strong> Build your first MCP server for your highest-value use case. Don&#8217;t try to migrate your entire tool suite at once. Get one server into production, measure the impact, then expand.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Use Strict JSON Schemas<\/strong><strong><br><\/strong> MCP servers receive inputs from LLMs. LLMs are creative and sometimes wrong. Validate every input against strict JSON schemas with additionalProperties: false. This prevents injection attacks and keeps servers behaving predictably.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Treat All Inputs as Untrusted<\/strong><strong><br><\/strong> Even though the input comes from Claude or another LLM, treat it as untrusted. Sanitize database queries. Validate file paths. Use allowlists for operations. The LLM is reasoning, not security-aware.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Implement Rate Limiting<\/strong><strong><br><\/strong> MCP servers often expose expensive operations (API calls, database queries, file I\/O). Implement rate limiting per client to prevent accidental resource exhaustion.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Use Transport Layer Security<\/strong><strong><br><\/strong> For remote MCP servers, use HTTPS with proper TLS validation. For sensitive operations, implement mutual TLS. Store API keys in environment variables, never in code.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Monitor and Log Carefully<\/strong><strong><br><\/strong> Log tool invocations for debugging and auditing. But be careful not to log sensitive data like passwords, API keys, or personal information. Implement separate logging channels for different sensitivity levels.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>A2A Implementation<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Define Clear Task Boundaries<\/strong><strong><br><\/strong> Each agent should have a clear area of responsibility. A Travel Booking Agent shouldn&#8217;t try to do finance approvals. Use Agent Cards to explicitly declare capabilities and boundaries.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Implement Robust Error Handling<\/strong><strong><br><\/strong> When an agent delegates work, things fail. Networks are unreliable. Remote agents crash. Implement exponential backoff, retry logic, and graceful degradation. Don&#8217;t let one failed sub-task take down the entire workflow.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Use Task Timeouts<\/strong><strong><br><\/strong> A2A tasks can run indefinitely. Set reasonable timeouts. If a task exceeds its timeout, mark it as failed and alert operations. This prevents zombie tasks from accumulating.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Monitor Agent Health<\/strong><strong><br><\/strong> Periodically check if agents are still healthy and responding. If an agent becomes unavailable, route its tasks to a fallback or queue them for retry.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Document Agent APIs<\/strong><strong><br><\/strong> Agent Cards describe capabilities, but they don&#8217;t tell other developers how to use them. Document expected inputs, likely outcomes, failure modes, and retry strategies.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><strong>Shared Security Practices<\/strong><\/h4>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Implement OAuth 2.0 for Enterprise Systems<\/strong><strong><br><\/strong> Both MCP and A2A support OAuth 2.0. Use it for any enterprise integration. API key auth is fine for development but not production at scale.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Segment Network Access<\/strong><strong><br><\/strong> Your MCP servers and A2A agents shouldn&#8217;t have broad network access. Use firewalls, VPCs, or service meshes to restrict who can talk to whom. A Finance Agent shouldn&#8217;t be able to reach HR databases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Audit All Access<\/strong><strong><br><\/strong> Log every tool invocation in MCP and every task delegation in A2A. Not for performance tuning. For compliance. You need to know which agent accessed which data when and why.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Implement Approval Workflows<\/strong><strong><br><\/strong> For sensitive operations, require human approval before execution. A tool for creating users should require admin approval. A task to transfer funds should require two agents to sign off.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Limitations and Realistic Considerations<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>MCP Limitations<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Passive Tool Problem<\/strong><strong><br><\/strong> MCP tools don&#8217;t reason. If you need sophisticated logic, you can&#8217;t embed it in the tool. The calling agent has to orchestrate complex multi-step workflows. This is fine for simple operations but becomes unwieldy for complex business logic.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Ecosystem Fragmentation<\/strong><strong><br><\/strong> 500+ MCP servers exist, but they&#8217;re not consistent. Different servers use different data formats, error responses, and authentication schemes. This creates integration friction in practice.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Vendor Lock-in Risk<\/strong><strong><br><\/strong> MCP is open but adoption concentrates around Anthropic&#8217;s ecosystem. If Anthropic changes direction, MCP&#8217;s future is uncertain. (This is improving as OpenAI and others adopt MCP, but concentration risk exists.)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>State Management<\/strong><strong><br><\/strong> MCP is stateless. If you need to maintain state across tool calls, the calling agent has to manage that state. For complex workflows with 10+ steps, this becomes error-prone.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>A2A Limitations<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Operational Complexity<\/strong><strong><br><\/strong> Running 50+ coordinated agents requires sophisticated infrastructure. You need agent registries, task routing, failure handling, and monitoring. This is not something you bolt onto existing systems.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Maturity Concerns<\/strong><strong><br><\/strong> A2A reached v1.0 in March 2026. It&#8217;s new. Most production deployments are from early adopters. Standard patterns and best practices are still emerging. If you&#8217;re risk-averse, wait another 12 months.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Adoption Fragmentation<\/strong><strong><br><\/strong> Multiple A2A-like protocols exist. Google&#8217;s A2A is dominant, but OpenAI and others have competing visions. By 2027, consolidation will happen, but we&#8217;re not there yet.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cost of Coordination<\/strong><strong><br><\/strong> Running multi-agent systems costs more than single-agent systems. More compute, more network traffic, more monitoring. Ensure your use case justifies the complexity.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Shared Limitations<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Limited to Frontier Models<\/strong><strong><br><\/strong> MCP and A2A assume you&#8217;re using frontier models (Claude, GPT-5.5, Gemini 2.5). Smaller or open-source models may not support these protocols. Check your model&#8217;s documentation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Debugging is Harder<\/strong><strong><br><\/strong> Multi-agent systems are harder to debug than single-agent systems. When something goes wrong, you don&#8217;t know if it&#8217;s MCP, A2A, the tool, or the agent. This requires better observability tooling, which is still maturing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Governance Maturity Needed<\/strong><strong><br><\/strong> Enterprises can&#8217;t run large-scale MCP and A2A systems without governance. Who owns which agent? Who deploys new tools? How are permissions managed? These organizational questions matter as much as technical ones.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Final Verdict<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The <strong>A2A vs MCP<\/strong> debate isn&#8217;t about choosing one protocol over the other-it&#8217;s about understanding the role each plays in modern AI architectures.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If you&#8217;re building a single AI agent that needs reliable access to tools, databases, APIs, or enterprise systems, <strong>MCP<\/strong> is the right place to start. It&#8217;s mature, production-ready, and simplifies integrations through a standardized interface.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As your applications evolve into networks of specialised agents that need to discover one another, delegate work, and coordinate complex workflows, <strong>A2A<\/strong> becomes the missing communication layer. It doesn&#8217;t replace MCP-it complements it by enabling seamless agent-to-agent collaboration.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For most enterprise AI systems, the future is <strong>MCP and A2A working together<\/strong>. MCP standardizes how agents access capabilities, while A2A standardizes how autonomous agents collaborate. Combined with strong security, governance, and observability, they provide a scalable foundation for building production-grade AI systems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Frequently Asked Questions<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Is one protocol better than the other?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">No. They solve different problems. Comparing them is like comparing HTTP and SSH. One is better for web browsers. The other is better for secure shells. Both are necessary, with different purposes.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Can I use MCP without A2A?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. If you have a single agent accessing tools and databases, MCP alone is sufficient. Only add A2A when you have multiple independent agents that need to collaborate.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>How do I choose an MCP server vs building my own?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Use existing servers first. Check the MCP registry at modelcontextprotocol.io. Only build your own if your specific use case isn&#8217;t covered. Building is 2-4 weeks. Integration is hours.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What&#8217;s the typical implementation timeline for A2A?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A proof of concept takes 4-8 weeks. Production deployment with proper governance, monitoring, and testing takes 3-6 months. Don&#8217;t underestimate the operational overhead.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Can MCP and A2A run on the same infrastructure?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. Many enterprises run MCP servers and A2A agents on the same Kubernetes cluster or cloud platform. They&#8217;re complementary, not competing.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>What&#8217;s the performance overhead of MCP?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Minimal. JSON-RPC over HTTP adds latency comparable to a single API call (10-100ms depending on network and tool complexity). If you&#8217;re making multiple tool calls, latency can add up.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Does A2A support real-time agent-to-agent communication?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Yes. A2A uses Server-Sent Events for streaming updates, enabling near-real-time task progress. Latency is typically 100-500ms, acceptable for most enterprise workflows.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Which protocol should startups prioritize?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">MCP. It&#8217;s mature, well-documented, and has a thriving ecosystem. Get your single-agent use cases working well first. Add A2A only when you have genuine multi-agent coordination needs.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>How does cost scale with these protocols?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">MCP cost scales with tool invocations (API calls, database queries). A2A cost scales with task volume and agent count. A 50-agent system costs significantly more than a 5-agent system, both in compute and operational overhead.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Are these protocols secure for sensitive data?<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Both support security (OAuth 2.0, mutual TLS, API key auth). But neither is inherently secure. Security depends entirely on implementation. Treat both as high-risk integration points and audit carefully.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When comparing A2A vs MCP, it&#8217;s easy to assume they&#8217;re competing protocols for AI agents. In reality, they solve different problems. As AI applications [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":1798,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2],"tags":[370,344,256,371,373,374,280,279,372],"content_cluster":[3],"content_type":[],"search_intent":[24],"tool_category":[28],"class_list":["post-1796","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-tools","tag-a2a","tag-agentic-ai","tag-ai-agents","tag-ai-interoperability","tag-ai-protocols","tag-langgraph","tag-mcp","tag-model-context-protocol","tag-multi-agent-systems","content_cluster-ai-tools","search_intent-informational","tool_category-ai-writing"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/posts\/1796","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Fcomments&post=1796"}],"version-history":[{"count":1,"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/posts\/1796\/revisions"}],"predecessor-version":[{"id":1799,"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/posts\/1796\/revisions\/1799"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=\/wp\/v2\/media\/1798"}],"wp:attachment":[{"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1796"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Fcategories&post=1796"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Ftags&post=1796"},{"taxonomy":"content_cluster","embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Fcontent_cluster&post=1796"},{"taxonomy":"content_type","embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Fcontent_type&post=1796"},{"taxonomy":"search_intent","embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Fsearch_intent&post=1796"},{"taxonomy":"tool_category","embeddable":true,"href":"https:\/\/www.five.reviews\/?rest_route=%2Fwp%2Fv2%2Ftool_category&post=1796"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}