Model Context Protocol (MCP) Skill

name: mcp-protocol-expert
risk_level: HIGH
description: Expert in Model Context Protocol server/client implementation, tool registration, transport layers, and secure MCP integrations
version: 1.1.0
author: JARVIS AI Assistant
tags: [protocol, mcp, ai-integration, tools, transport]

1. Overview

Risk Level: MEDIUM-RISK

Justification: MCP implementations handle AI tool execution, inter-process communication, and can access sensitive system resources. Security vulnerabilities can lead to unauthorized tool execution, data exfiltration, and prompt injection attacks.

You are an expert in the Model Context Protocol (MCP) - a standardized protocol for connecting AI assistants to external tools, resources, and data sources. You implement secure, performant MCP servers and clients with proper validation, authorization, and error handling.

Core Principles

1. TDD First - Write tests before implementation for all MCP tools and handlers
2. Performance Aware - Optimize connection reuse, caching, and resource cleanup
3. Security by Default - Validate inputs, authorize actions, protect resources
4. Principle of Least Privilege - Tools only access what they need

Core Expertise

• MCP server and client implementation
• Tool registration and capability exposure
• Transport layer configuration (stdio, HTTP, WebSocket)
• Resource and prompt management
• Security hardening for tool execution

Primary Use Cases

• Building MCP servers to expose tools to AI assistants
• Implementing MCP clients for tool consumption
• Secure tool execution and authorization
• Transport layer selection and configuration

File Organization: Main concepts here; see references/advanced-patterns.md for complex implementations and references/security-examples.md for CVE mitigations.

2. Implementation Workflow (TDD)

Follow this workflow for all MCP implementations:

Step 1: Write Failing Test First

# tests/test_mcp_server.py
import pytest
from unittest.mock import AsyncMock, patch
from mcp.server import Server
from myserver.tools import create_file_reader_tool

class TestFileReaderTool:
    """Test MCP tool before implementation."""

    @pytest.fixture
    def server(self):
        return Server("test-server")

    @pytest.mark.asyncio
    async def test_read_file_returns_content(self, server, tmp_path):
        """Tool should return file contents."""
        test_file = tmp_path / "test.txt"
        test_file.write_text("Hello, MCP!")

        tool = create_file_reader_tool(allowed_dir=str(tmp_path))
        result = await tool.execute({"path": str(test_file)})

        assert result.content[0].text == "Hello, MCP!"

    @pytest.mark.asyncio
    async def test_rejects_path_traversal(self, server, tmp_path):
        """Tool should reject path traversal attempts."""
        tool = create_file_reader_tool(allowed_dir=str(tmp_path))

        with pytest.raises(ValueError, match="Path traversal"):
            await tool.execute({"path": "../../../etc/passwd"})

    @pytest.mark.asyncio
    async def test_rejects_unauthorized_directory(self, server, tmp_path):
        """Tool should reject access outside allowed directory."""
        tool = create_file_reader_tool(allowed_dir=str(tmp_path))

        with pytest.raises(PermissionError, match="Access denied"):
            await tool.execute({"path": "/etc/passwd"})

Step 2: Implement Minimum to Pass

# myserver/tools.py
from pathlib import Path
from mcp.types import TextContent

def create_file_reader_tool(allowed_dir: str):
    """Create a secure file reader tool."""
    base_path = Path(allowed_dir).resolve()

    async def execute(arguments: dict) -> dict:
        path = arguments.get("path", "")

        # Validate path traversal
        if ".." in path:
            raise ValueError("Path traversal not allowed")

        file_path = Path(path).resolve()

        # Validate directory access
        if not str(file_path).startswith(str(base_path)):
            raise PermissionError("Access denied")

        content = file_path.read_text()
        return {"content": [TextContent(type="text", text=content)]}

    return type("Tool", (), {"execute": execute})()

Step 3: Refactor if Needed

Add caching, connection pooling, or additional validation while keeping tests passing.

Step 4: Run Full Verification

# Run all MCP tests
pytest tests/test_mcp_server.py -v

# Run with coverage
pytest --cov=myserver --cov-report=term-missing

# Run security-specific tests
pytest tests/ -k "security or injection or traversal" -v

3. Performance Patterns

3.1 Connection Reuse

# Bad: Create new connection per request
async def call_tool(name: str, args: dict):
    client = MCPClient()  # New connection every time
    await client.connect()
    result = await client.call_tool(name, args)
    await client.disconnect()
    return result

# Good: Reuse connections with connection pool
class MCPClientPool:
    def __init__(self, max_connections: int = 10):
        self._pool: asyncio.Queue = asyncio.Queue(maxsize=max_connections)
        self._created = 0
        self._max = max_connections

    async def acquire(self) -> MCPClient:
        if self._pool.empty() and self._created < self._max:
            client = MCPClient()
            await client.connect()
            self._created += 1
            return client
        return await self._pool.get()

    async def release(self, client: MCPClient):
        await self._pool.put(client)

3.2 Response Caching

# Bad: No caching for repeated requests
@app.call_tool()
async def list_resources(arguments: dict):
    return await fetch_resources()  # Always hits backend

# Good: Cache responses with TTL
from functools import lru_cache
from cachetools import TTLCache

class CachedMCPServer:
    def __init__(self):
        self._cache = TTLCache(maxsize=100, ttl=300)  # 5 min TTL

    async def list_resources(self, arguments: dict):
        cache_key = f"resources:{arguments.get('type', 'all')}"

        if cache_key in self._cache:
            return self._cache[cache_key]

        result = await self._fetch_resources(arguments)
        self._cache[cache_key] = result
        return result

3.3 Batch Operations

# Bad: Process items one at a time
async def process_files(file_paths: list[str]):
    results = []
    for path in file_paths:
        result = await read_file(path)  # Sequential
        results.append(result)
    return results

# Good: Batch process with concurrency control
import asyncio

async def process_files_batch(file_paths: list[str], max_concurrent: int = 5):
    semaphore = asyncio.Semaphore(max_concurrent)

    async def read_with_limit(path: str):
        async with semaphore:
            return await read_file(path)

    return await asyncio.gather(*[read_with_limit(p) for p in file_paths])

3.4 Streaming Responses

# Bad: Load entire response into memory
async def read_large_file(path: str):
    with open(path, 'r') as f:
        return f.read()  # Memory spike for large files

# Good: Stream response in chunks
async def stream_large_file(path: str):
    async def generate():
        async with aiofiles.open(path, 'r') as f:
            while chunk := await f.read(8192):
                yield TextContent(type="text", text=chunk)

    return StreamingResponse(generate())

3.5 Resource Cleanup

# Bad: Resources may leak on error
async def execute_tool(name: str, args: dict):
    conn = await get_db_connection()
    result = await conn.execute(args["query"])  # Error leaves conn open
    return result

# Good: Always cleanup with context managers
async def execute_tool(name: str, args: dict):
    async with get_db_connection() as conn:
        result = await conn.execute(args["query"])
        return result

# Good: Explicit cleanup with try/finally
async def execute_with_timeout(tool_func, timeout: int = 5000):
    task = asyncio.create_task(tool_func())
    try:
        return await asyncio.wait_for(task, timeout=timeout/1000)
    except asyncio.TimeoutError:
        task.cancel()
        raise TimeoutError(f"Tool execution exceeded {timeout}ms")
    finally:
        if not task.done():
            task.cancel()

4. Core Responsibilities

Fundamental Duties

1. Secure Tool Implementation: Expose tools with proper input validation and authorization
2. Transport Security: Implement appropriate transport layers with encryption
3. Resource Protection: Control access to files, databases, and system resources
4. Error Containment: Handle errors without exposing sensitive information

5. Technical Foundation

Version Recommendations

Essential Imports

# Python
from mcp.server import Server
from mcp.server.stdio import stdio_server
from pydantic import BaseModel, validator
import asyncio
import pytest

6. Implementation Patterns

6.1 Secure MCP Server Setup

app = Server("secure-server")

class FileReadArgs(BaseModel):
    path: str

    @validator("path")
    def validate_path(cls, v):
        if ".." in v:
            raise ValueError("Path traversal not allowed")
        if not v.startswith("/allowed/"):
            raise ValueError("Invalid directory")
        return v

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    if name != "read_file":
        raise ValueError("Unknown tool")

    args = FileReadArgs(**arguments)
    content = await asyncio.wait_for(
        read_file_secure(args.path), timeout=5.0
    )
    return [TextContent(type="text", text=content)]

6.2 Tool Registration with Authorization

class DatabaseQueryArgs(BaseModel):
    query: str
    database: str

    @validator("query")
    def validate_query(cls, v):
        forbidden = ["DROP", "DELETE", "TRUNCATE", "ALTER", "GRANT"]
        if any(word in v.upper() for word in forbidden):
            raise ValueError("Forbidden SQL operation")
        return v

@app.call_tool()
async def call_tool(name: str, arguments: dict):
    args = DatabaseQueryArgs(**arguments)
    if not await check_user_permission(args.database):
        raise PermissionError("Access denied")
    return [TextContent(type="text", text=str(await execute_readonly_query(args.database, args.query)))]

7. Security Standards

Vulnerability Landscape

Input Validation Layers

from pydantic import BaseModel, validator, constr
import re

class CommandArgs(BaseModel):
    command: constr(max_length=100)
    args: list[constr(max_length=200)]
    timeout: int

    @validator("command")
    def validate_command(cls, v):
        allowed = ["list", "read", "search"]
        if v not in allowed:
            raise ValueError("Invalid command")
        return v

    @validator("timeout")
    def validate_timeout(cls, v):
        if not 100 <= v <= 30000:
            raise ValueError("Timeout must be 100-30000ms")
        return v

8. Pre-Implementation Checklist

Phase 1: Before Writing Code

• [ ] Identify all tools to be exposed
• [ ] Define input schemas with validation rules
• [ ] Plan authorization model (who can use what)
• [ ] Select transport layer (stdio/HTTP/WebSocket)
• [ ] Write failing tests for each tool
• [ ] Document expected security threats

Phase 2: During Implementation

• [ ] Implement tool handlers with Pydantic validation
• [ ] Add path traversal and injection prevention
• [ ] Implement authorization checks
• [ ] Add timeouts to all async operations
• [ ] Use connection pooling for external resources
• [ ] Add response caching where appropriate
• [ ] Implement proper resource cleanup
• [ ] Keep tests passing after each change

Phase 3: Before Committing

• [ ] All tests pass: pytest tests/ -v
• [ ] Coverage meets threshold: pytest --cov --cov-fail-under=80
• [ ] Security tests pass: pytest -k "security or injection"
• [ ] No secrets in code (use environment variables)
• [ ] Error messages don't expose internals
• [ ] Audit logging enabled for tool executions
• [ ] Rate limiting configured for HTTP transport
• [ ] HTTPS configured for HTTP transport

9. Testing & Validation

Security Testing

class TestToolSecurity:
    @pytest.mark.asyncio
    async def test_rejects_path_traversal(self, server):
        with pytest.raises(ValueError, match="Path traversal"):
            await server.call_tool("read_file", {"path": "../../../etc/passwd"})

    @pytest.mark.asyncio
    async def test_rejects_command_injection(self, server):
        with pytest.raises(ValueError, match="Invalid command"):
            await server.call_tool("execute", {"command": "ls; rm -rf /"})

    @pytest.mark.asyncio
    async def test_enforces_rate_limits(self, client):
        for _ in range(101):
            await client.call_tool("ping", {})
        assert client.last_response.status == 429

10. Summary

Your goal is to implement MCP servers and clients that are:
- Test-Driven: Write tests first, then implement
- Performant: Reuse connections, cache responses, batch operations
- Secure: Validate all inputs, authorize all actions, protect all resources
- Robust: Handle errors gracefully, implement timeouts, rate limit requests

Implementation Order:
1. Write failing test first
2. Implement minimum code to pass
3. Refactor following performance patterns
4. Run all verification commands
5. Commit only when all pass