Agent Protocols

Agent Protocols

Fixes Category 4 (Agent Protocols, 15% weight) issues from IsAgentReady.com. This category checks whether AI agents can discover and use your website's functionality directly through standardized protocols.

When to Use

• Site has forms or interactive features that AI agents should use
• Building an API that AI agents should discover and call
• Running AI agent services that need to advertise capabilities
• IsAgentReady.com scanner reports low Agent Protocols score
• Asked to "add WebMCP", "implement A2A", "create agent card", "add MCP discovery", "make site agent-ready"

When NOT to Use

• Site is purely static content with no interactive features or APIs
• Implementing security headers or CSP → use security-trust skill
• Adding structured data / Schema.org → use structured-data skill
• Improving semantic HTML or SSR → use content-semantics skill
• Adding robots.txt, sitemap, llms.txt → use ai-content-discovery skill

Checkpoints Overview

Total: 100 points

Checkpoint 4.1: WebMCP Declarative API (25 pts)

WebMCP is the W3C proposal for exposing website functionality directly to AI agents. Declarative tool annotations on forms let agents perform actions (search, purchase, book) without screen scraping.

What the scanner checks: HTML forms/inputs with both tool-name AND tool-description attributes. Also detects page manifest and imperative API (partial credit).

Scoring: Declarative attributes = 25 pts | Page manifest pts | Imperative API pts | None = 0

Fix workflow

Step 1: Identify interactive forms on your site

Find forms that perform useful actions: search, contact, booking, purchase, filtering, login.

Step 2: Add WebMCP declarative attributes to each form

<!-- Search form -->
<form action="/search" method="GET"
      tool-name="search-products"
      tool-description="Search the product catalog by keyword, category, or price range">
  <input type="text" name="query"
         tool-param-description="Search keywords">
  <select name="category"
          tool-param-description="Product category filter">
    <option value="">All categories</option>
    <option value="electronics">Electronics</option>
    <option value="books">Books</option>
  </select>
  <button type="submit">Search</button>
</form>

<!-- Contact form -->
<form action="/contact" method="POST"
      tool-name="send-message"
      tool-description="Send a message to customer support">
  <input type="text" name="name"
         tool-param-description="Full name of the sender">
  <input type="email" name="email"
         tool-param-description="Email address for replies">
  <textarea name="message"
            tool-param-description="Message content"></textarea>
  <button type="submit">Send</button>
</form>

Step 3 (optional): Add a page-level manifest for richer metadata

<script type="application/json" id="webmcp">
{
  "tools": [
    {
      "name": "search-products",
      "description": "Search the product catalog",
      "parameters": [
        {"name": "query", "type": "string", "required": true},
        {"name": "category", "type": "string", "required": false}
      ]
    }
  ]
}
</script>

Key rules:

• MUST have BOTH tool-name AND tool-description on the form — one alone won't score
• Both hyphenated (tool-name) and non-hyphenated (toolname) forms are accepted
• tool-param-description on inputs is optional but recommended for richer context
• Works on <form>, <input>, <select>, and <textarea> elements

See references/webmcp-guide.md for imperative API, page manifests, and advanced patterns.

Checkpoint 4.2: WebMCP Manifest (10 pts)

A WebMCP manifest enables pre-navigation discovery — AI agents can learn what tools your site offers before loading any page.

What the scanner checks: Valid JSON at /.well-known/webmcp or /.well-known/webmcp.json.

Fix workflow

Step 1: Create the manifest file

{
  "spec": "webmcp/0.1",
  "tools": [
    {
      "name": "search-products",
      "description": "Search the product catalog by keyword",
      "url": "/search",
      "method": "GET",
      "parameters": [
        {"name": "q", "type": "string", "description": "Search query", "required": true}
      ]
    },
    {
      "name": "get-product",
      "description": "Get product details by ID",
      "url": "/api/products/{id}",
      "method": "GET",
      "parameters": [
        {"name": "id", "type": "string", "description": "Product ID", "required": true}
      ]
    }
  ]
}

Step 2: Serve at the well-known path

The file must be accessible at /.well-known/webmcp or /.well-known/webmcp.json and return Content-Type: application/json.

• Static sites (Nginx/Apache): Place file in .well-known/ directory
• Node/Express: app.get('/.well-known/webmcp.json', (req, res) => res.json(manifest))
• Rails/Django/Phoenix: Add a route that returns the JSON
• Serverless: Add a function at the path

Bonus: Include a "spec" field (e.g., "webmcp/0.1") for version identification.

See references/webmcp-guide.md for manifest schema details.

Checkpoint 4.3: A2A Agent Card (25 pts)

Google's Agent-to-Agent (A2A) protocol enables autonomous agent-to-agent communication. An Agent Card advertises your agent's capabilities to other AI systems.

What the scanner checks: /.well-known/agent.json with all 4 required fields: name, description, url, capabilities.

Scoring: All 4 fields = 25 pts | 2-3 fields = 15 pts | <2 fields = 0 pts

Fix workflow

Step 1: Create the Agent Card

{
  "name": "Acme Support Agent",
  "description": "AI assistant for customer support, order tracking, and product recommendations",
  "url": "https://example.com/agent",
  "capabilities": {
    "streaming": true,
    "pushNotifications": false,
    "stateTransitionHistory": false
  },
  "skills": [
    {
      "id": "order-tracking",
      "name": "Order Tracking",
      "description": "Track order status and delivery updates"
    },
    {
      "id": "product-search",
      "name": "Product Search",
      "description": "Search and recommend products"
    }
  ],
  "defaultInputModes": ["text/plain"],
  "defaultOutputModes": ["text/plain", "application/json"]
}

Step 2: Serve at /.well-known/agent.json

Must return valid JSON with Content-Type: application/json.

Required fields (all 4 needed for full score):

• name — Human-readable agent name
• description — What the agent does
• url — Endpoint where the agent accepts requests
• capabilities — Object describing what the agent supports

See references/a2a-mcp-guide.md for all A2A fields and advanced configuration.

Checkpoint 4.4: MCP Discovery (10 pts)

The Model Context Protocol (MCP) by Anthropic is the standard for connecting AI assistants to external tools and data. A discovery document lets AI systems find your MCP server.

What the scanner checks: /.well-known/mcp.json returns a valid non-empty JSON object.

Fix workflow

Step 1: Create the MCP discovery document

{
  "mcpServers": {
    "main": {
      "url": "https://example.com/mcp",
      "name": "Example MCP Server",
      "description": "Provides product search, order management, and customer data tools"
    }
  }
}

Step 2: Serve at /.well-known/mcp.json

Must return valid JSON with Content-Type: application/json. The JSON must be a non-empty object — empty {} will fail.

See references/a2a-mcp-guide.md for MCP discovery patterns and server configuration.

Checkpoint 4.5: OpenAPI / API Documentation (20 pts)

OpenAPI specifications let AI agents understand and call your API programmatically. AI coding assistants and agent frameworks use OpenAPI to generate correct API calls automatically.

What the scanner checks: Non-HTML response at /openapi.json, /openapi.yaml, /swagger.json, /api-docs, or /.well-known/openapi.

Scoring: OpenAPI 3.x = 20 pts | Swagger 2.x = 15 pts | Endpoint exists but unparseable = 10 pts | Not found = 0

Fix workflow

Step 1: Create an OpenAPI 3.x specification

{
  "openapi": "3.1.0",
  "info": {
    "title": "My Site API",
    "version": "1.0.0",
    "description": "API for product search and order management"
  },
  "servers": [
    {"url": "https://example.com"}
  ],
  "paths": {
    "/api/search": {
      "get": {
        "operationId": "searchProducts",
        "summary": "Search products by keyword",
        "parameters": [
          {
            "name": "q",
            "in": "query",
            "required": true,
            "schema": {"type": "string"},
            "description": "Search query"
          },
          {
            "name": "limit",
            "in": "query",
            "schema": {"type": "integer", "default": 20},
            "description": "Max results to return"
          }
        ],
        "responses": {
          "200": {
            "description": "Search results",
            "content": {
              "application/json": {
                "schema": {
                  "type": "object",
                  "properties": {
                    "results": {
                      "type": "array",
                      "items": {"$ref": "#/components/schemas/Product"}
                    }
                  }
                }
              }
            }
          }
        }
      }
    }
  },
  "components": {
    "schemas": {
      "Product": {
        "type": "object",
        "properties": {
          "id": {"type": "string"},
          "name": {"type": "string"},
          "price": {"type": "number"}
        }
      }
    }
  }
}

Step 2: Serve at a standard path

Serve at /openapi.json (preferred) or any of: /openapi.yaml, /swagger.json, /api-docs, /.well-known/openapi.

Key rules:

• Must NOT return HTML — the scanner ignores HTML responses at these endpoints
• Include "openapi": "3.x.x" for full score; "swagger": "2.0" gets partial (15 pts)
• Use operationId on each operation — AI agents use these as function names

See references/a2a-mcp-guide.md for OpenAPI best practices for AI agents.

Checkpoint 4.6: agents.json (10 pts)

agents.json provides a directory of AI agent endpoints on your site. It helps agent orchestration systems discover and connect to your available AI services.

What the scanner checks: Valid non-empty JSON at /.well-known/agents.json or /agents.json.

Fix workflow

Step 1: Create agents.json

{
  "agents": [
    {
      "name": "Customer Support Agent",
      "description": "Handles customer inquiries, order tracking, and returns",
      "url": "https://example.com/agents/support",
      "protocol": "a2a",
      "capabilities": ["text", "streaming"]
    },
    {
      "name": "Product Recommendation Agent",
      "description": "Recommends products based on preferences and browsing history",
      "url": "https://example.com/agents/recommendations",
      "protocol": "a2a",
      "capabilities": ["text"]
    }
  ]
}

Step 2: Serve at /.well-known/agents.json or /agents.json

Must return valid, non-empty JSON. Empty {} or [] will fail.

Quick-start: Implement all 6 checkpoints

For a site that has a search form and a basic API, here's the minimum to pass all checkpoints:

1. Annotate your forms (4.1)

Add tool-name and tool-description to every interactive <form>.

2. Create /.well-known/ directory with 4 JSON files

.well-known/
  webmcp.json      # 4.2 — tool manifest
  agent.json       # 4.3 — A2A Agent Card
  mcp.json         # 4.4 — MCP discovery
  agents.json      # 4.6 — agent directory

3. Serve OpenAPI spec (4.5)

Serve your API spec at /openapi.json.

4. Configure your web server

Ensure all /.well-known/* paths return Content-Type: application/json and are not blocked by your WAF, CDN, or middleware.

Key Gotchas

Common mistakes that cause checkpoint failures:

1. WebMCP needs BOTH attributes — tool-name alone scores 0
2. A2A Agent Card needs all 4 required fields — missing capabilities drops to 15 pts
3. HTML at API endpoints is ignored — Swagger UI pages don't count as OpenAPI
4. Empty JSON objects fail — {} at mcp.json scores 0
5. Swagger 2.x gets partial credit — upgrade to OpenAPI 3.x for full score
6. Both hyphenated and non-hyphenated work — tool-name and toolname both accepted

See references/gotchas.md for detailed correct vs incorrect examples of each.

References

• WebMCP implementation guide — Declarative API, page manifests, imperative API, well-known manifest
• A2A, MCP & OpenAPI guide — A2A Agent Cards, MCP discovery, OpenAPI specs, agents.json, protocol comparison
• Common gotchas — Pitfalls with correct vs incorrect examples

Instructions

1. Identify failing checkpoints from the IsAgentReady.com scan results
2. Determine which protocols apply — WebMCP for interactive forms, A2A/MCP for AI agent services, OpenAPI for APIs
3. Follow the fix workflow for each failing checkpoint above
4. Apply the code examples — adapt tool names, descriptions, and endpoints to the user's site
5. Verify that /.well-known/* paths return valid JSON with correct Content-Type
6. Re-scan at isagentready.com to confirm improvements

If $ARGUMENTS is provided, interpret it as the URL to fix or the specific checkpoint to address.