mcp-patterns

name	mcp-patterns
description	MCP server design: tool schemas, resources, stdio/SSE, capability negotiation. Triggers: MCP, Model Context Protocol, JSON-RPC, stdio, SSE, Claude Desktop.
effort	medium
user-invocable	false
allowed-tools	Read

MCP Patterns Skill

MCP Specification (2025-06-18)

Core Concepts

Concept	Description
Server	Exposes tools, resources, prompts to clients
Client	Connects to servers, invokes tools
Transport	Communication layer (stdio, HTTP, SSE)
Tool	Executable function with JSON Schema
Resource	Read-only data (files, URLs)
Prompt	Reusable prompt template

---

Tool Definition Pattern

// TypeScript with @modelcontextprotocol/sdk
server.setRequestHandler(ListToolsRequestSchema, async () => ({
  tools: [{
    name: "search_kb",
    description: "Search the knowledge base",
    inputSchema: {
      type: "object",
      properties: {
        query: {
          type: "string",
          description: "Search query"
        },
        limit: {
          type: "number",
          description: "Max results",
          default: 10
        }
      },
      required: ["query"]
    },
    annotations: {
      readOnlyHint: true,      // Doesn't modify state
      idempotentHint: true,    // Same input = same output
      openWorldHint: false     // Bounded result set
    }
  }]
}));

Tool Annotations

Annotation	Meaning	Use When
readOnlyHint	No side effects	Read operations
destructiveHint	Deletes/modifies data	Write operations
idempotentHint	Safe to retry	GET-like operations
openWorldHint	Results may change	External API calls

---

How to Write a Tool Description

The description is the only signal the model uses to route a request. The schema constrains the call; the description decides whether the call happens at all. Treat it as a routing contract, not API prose. A good one has five parts, in this order:

1. One-line purpose — what the tool does, in plain terms. Lead with a verb. Search indexed knowledge-base documents and return ranked passages. Skip the HTTP verb and endpoint; calls GET /v2/search tells the model nothing about intent.
2. WHEN TO USE — concrete trigger phrasings the user might say, not abstract categories. List the actual shapes: "find docs about X", "what does the KB say about Y", "look up the runbook for Z". Models match on surface form, so give them surface forms.
3. WHEN NOT TO USE — the section that does most of the disambiguation work. Name the near-miss tools and the boundary that separates them. This is where you prevent the model from firing the wrong tool on a request that looks similar. Empty WHEN NOT TO USE = the tool is under-specified.
4. CRITICAL — one line for the single non-obvious failure mode. The constraint a reader would not guess from the schema: a required ordering, an ID that must come from another call, a cost/irreversibility warning. One line, not a checklist.
5. Self-test — close with a question the model can apply to itself to decide fit. Ask: is the user looking up existing content, or asking me to create new content? This tool is read-only — if they want to create, stop.

Why negative examples outweigh positive ones

Positive triggers tell the model when a tool could apply; negative ones are what stop it firing on overlapping requests. When two tools have similar purposes (search_kb vs search_code, get_document vs list_documents), the only thing keeping the model off the wrong one is each description naming the other and drawing the line. Budget more words for the boundary than the bullseye.

Disambiguating near-miss tools

When tools overlap, make each WHEN NOT TO USE point at its neighbour and state the discriminator explicitly:

search_kb
  WHEN NOT TO USE: do not use to fetch a document you already have the id for —
    that is get_document. Use search_kb only when you need to discover *which*
    document, by meaning or keyword.

get_document
  WHEN NOT TO USE: do not use to find a document by topic or keyword — you must
    already hold an exact id (from search_kb results). For discovery, use search_kb.

Worked example

name: cancel_workflow
description: |
  Stop a running agent workflow and discard its in-flight results.

  WHEN TO USE: the user says "cancel the workflow", "stop run abc123",
    "kill that job", or asks to halt a workflow that get_workflow_status
    reports as RUNNING.

  WHEN NOT TO USE:
    - To inspect progress without stopping — use get_workflow_status.
    - To start a fresh run — use start_workflow.
    - On a workflow already in a terminal state (COMPLETED/FAILED) — the call
      is a no-op and signals the model misread the status.

  CRITICAL: cancellation is irreversible and drops partial output. Confirm the
    workflowId came from list_workflows or get_workflow_status — never type one
    from memory.

  Self-test: am I stopping work that is genuinely still RUNNING, or did I confuse
  "check status" with "cancel"? If I have not seen a RUNNING status, do not call this.

A description that survives this rubric routes correctly without the model reading your source. One that skips WHEN NOT TO USE will misfire the moment a second, similar tool exists in the same server.

---

Transport Patterns

1. stdio (Default)

import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio";

const transport = new StdioServerTransport();
await server.connect(transport);

2. HTTP with SSE

# FastAPI implementation
from fastapi import FastAPI
from sse_starlette.sse import EventSourceResponse

app = FastAPI()

@app.get("/mcp/sse")
async def sse_endpoint():
    async def event_generator():
        while True:
            # Yield MCP events
            yield {"event": "message", "data": json.dumps(response)}
    return EventSourceResponse(event_generator())

@app.post("/mcp")
async def jsonrpc_endpoint(request: Request):
    body = await request.json()
    response = await handle_jsonrpc(body)
    return JSONResponse(response)

3. Streamable HTTP (Modern)

Single /mcp endpoint handling GET (SSE) and POST (JSON-RPC):

@app.api_route("/mcp", methods=["GET", "POST"])
async def mcp_endpoint(request: Request):
    if request.method == "GET":
        # SSE streaming
        return EventSourceResponse(stream_generator())
    else:
        # JSON-RPC
        body = await request.json()
        return JSONResponse(await handle_jsonrpc(body))

---

JSON-RPC 2.0 Pattern

Request Format

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "search_kb",
    "arguments": {"query": "test", "limit": 5}
  }
}

Response Format

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "content": [
      {"type": "text", "text": "Search results..."}
    ]
  }
}

Error Format

{
  "jsonrpc": "2.0",
  "id": 1,
  "error": {
    "code": -32601,
    "message": "Method not found"
  }
}

Standard Error Codes

Code	Meaning
-32700	Parse error
-32600	Invalid request
-32601	Method not found
-32602	Invalid params
-32603	Internal error

---

Completion Support

Enable intelligent argument suggestions:

server.setRequestHandler(CompletionCompleteRequestSchema, async (request) => {
  const { argument } = request.params;

  if (argument.name === "service") {
    return {
      completion: {
        values: ["nginx", "postgresql", "redis", "qdrant"],
        hasMore: false
      }
    };
  }

  return { completion: { values: [], hasMore: false } };
});

---

Session Management

// Generate secure session ID
const sessionId = crypto.randomUUID();

// Validate Origin header
const allowedOrigins = ["http://localhost:3000", "https://claude.ai"];
if (!allowedOrigins.includes(request.headers.origin)) {
  throw new Error("Invalid origin");
}

// Session storage (don't expose to client)
const sessions = new Map<string, SessionData>();

---

Batching Support

Handle multiple requests in single HTTP call:

async def handle_jsonrpc(body):
    if isinstance(body, list):
        # Batch request
        return [await process_single(req) for req in body]
    else:
        # Single request
        return await process_single(body)

---

Best Practices

Security

• Validate all inputs against JSON Schema
• Validate Origin header
• Implement rate limiting
• Use environment variables for secrets
• No secrets in logs or errors

Performance

• Connection pooling
• Response caching where appropriate
• Streaming for large responses
• Batch support for efficiency

Logging

• Log to stderr (never stdout)
• Structured JSON logs
• Request/response tracing
• Error logging with context

Documentation

• Document all tools clearly
• Include example usage
• Version your API
• Maintain changelog

---

RAG-MCP MCP Tools

Tool	Description
smart_query	Primary search with auto-routing
hybrid_search_kb	Raw vector + text search
get_document	Full document content
crag_search	Self-correcting search
multi_hop_search	Complex reasoning
start_workflow	Start agent workflow
get_workflow_status	Check workflow progress
list_workflows	List all workflows
cancel_workflow	Cancel workflow