Skip to main content Skills Marktplatz Entdecken und erkunden Sie KI-Skills, die von der Community erstellt wurden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Prompt-Details anzeigen Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
npx skills add https://github.com/bobmatnyc/claude-mpm --skill build-mcp-serverDer Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.
Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.
ZIP herunterladen Herunterladen... Mehr aus diesem Repository
Verwandte Berufe SOC
Basierend auf der SOC-Berufsklassifikation
name build-mcp-server description MCP (Model Context Protocol) - Build AI-native servers with tools, resources, and prompts. TypeScript/Python SDKs for Claude Desktop integration. version 1.0.0 category toolchain author Claude MPM Team license MIT progressive_disclosure {"entry_point":{"summary":"Build MCP servers: expose tools (functions), resources (data), prompts (templates). TypeScript/Python SDKs. Claude Desktop native integration.","when_to_use":"Creating AI integrations, exposing APIs to LLMs, building Claude Desktop extensions, server-side AI tooling, custom data connectors","quick_start":"1. npx @modelcontextprotocol/create-server@latest my-server 2. Define tools/resources 3. Add to Claude Desktop config 4. Restart Claude"}} context_limit 700 tags ["mcp","model-context-protocol","ai","llm","claude","tools","resources","prompts","typescript","python","integration"] requires_tools []
MCP (Model Context Protocol) - AI-Native Server Development
Overview
Model Context Protocol (MCP) is an open standard for connecting AI assistants to external data sources and tools. Build servers that expose tools (functions LLMs can call), resources (data LLMs can read), and prompts (templates LLMs can use).
Key Concepts :
Tools : Functions LLMs can execute (read files, query APIs, run commands)Resources : Data sources LLMs can access (files, databases, APIs)Prompts : Reusable templates with arguments for common tasksClient-Server : MCP servers expose capabilities, clients (like Claude Desktop) consume themTransport : STDIO (local), SSE (Server-Sent Events), HTTP (network)
Official SDKs :
TypeScript: @modelcontextprotocol/sdk
Python: mcp
Installation :
# TypeScript server
npx @modelcontextprotocol/create-server@latest my-server
cd my-server && npm install
# Python server
pip install mcp
# Or use uv (recommended)
uv pip install mcp
Quick Start - TypeScript Server
1. Create Server with CLI
# Interactive setup
npx @modelcontextprotocol/create-server@latest my-filesystem-server
# Options prompt:
# - Server name: my-filesystem-server
# - Language: TypeScript
# - Include example tools: Yes
2. Define Tools
// src/index.ts
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import {
CallToolRequestSchema,
ListToolsRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
import * as fs from "fs/promises";
import * as path from "path";
const server = new Server(
{
name: "filesystem-server",
version: "1.0.0",
},
{
capabilities: {
tools: {},
},
}
);
// List available tools
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: "read_file",
description: "Read contents of a file",
inputSchema: {
type: "object",
properties: {
path: {
type: "string",
description: "Path to the file to read",
},
},
required: ["path"],
},
},
{
name: "list_directory",
description: "List contents of a directory",
inputSchema: {
type: "object",
properties: {
path: {
type: "string",
description: "Directory path to list",
},
},
required: ["path"],
},
},
],
};
});
// Handle tool calls
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
switch (name) {
case "read_file": {
const filePath = args.path as string;
const content = await fs.readFile(filePath, "utf-8");
return {
content: [{ type: "text", text: content }],
};
}
case "list_directory": {
const dirPath = args.path as string;
const entries = await fs.readdir(dirPath, { withFileTypes: true });
const listing = entries
.map((entry) => `${entry.isDirectory() ? "📁" : "📄"} ${entry.name}`)
.join("\n");
return {
content: [{ type: "text", text: listing }],
};
}
default:
throw new Error(`Unknown tool: ${name}`);
}
});
// Start server
async function main() {
const transport = new StdioServerTransport();
await server.connect(transport);
console.error("Filesystem MCP server running on stdio");
}
main();
3. Configure Claude Desktop // ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
// %APPDATA%/Claude/claude_desktop_config.json (Windows)
{
"mcpServers": {
"filesystem": {
"command": "node",
"args": ["/absolute/path/to/my-filesystem-server/build/index.js"]
}
}
}
4. Build and Test # Build TypeScript
npm run build
# Restart Claude Desktop (Cmd+Q and reopen)
# Server appears in 🔌 menu
Quick Start - Python Server
1. Create Server # server.py
import asyncio
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import json
import os
# Create server instance
app = Server("filesystem-server")
# Define tools
@app.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="read_file",
description="Read contents of a file",
inputSchema={
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path to read"}
},
"required": ["path"],
},
),
Tool(
name="list_directory",
description="List directory contents",
inputSchema={
"type": "object",
"properties": {
"path": {"type": "string", "description": "Directory path"}
},
"required": ["path"],
},
),
]
# Handle tool calls
@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
if name == "read_file":
file_path = arguments["path"]
with open(file_path, "r") as f:
content = f.read()
return [TextContent(type="text", text=content)]
elif name == "list_directory":
dir_path = arguments["path"]
entries = os.listdir(dir_path)
listing = "\n".join(
f"{'📁' if os.path.isdir(os.path.join(dir_path, e)) else '📄'} {e}"
for e in entries
)
return [TextContent(type="text", text=listing)]
else:
raise ValueError(f"Unknown tool: {name}")
# Start server
async def main():
async with stdio_server() as (read_stream, write_stream):
await app.run(read_stream, write_stream, app.create_initialization_options())
if __name__ == "__main__":
asyncio.run(main())
2. Configure Claude Desktop {
"mcpServers": {
"filesystem": {
"command": "python",
"args": ["/absolute/path/to/server.py"]
}
}
}
3. Test # Test server standalone
python server.py
# Restart Claude Desktop
# Tools appear in 🔌 menu
Resources - Exposing Data to LLMs Resources provide read-only access to data sources.
TypeScript Resource Example import {
ListResourcesRequestSchema,
ReadResourceRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
// List available resources
server.setRequestHandler(ListResourcesRequestSchema, async () => {
return {
resources: [
{
uri: "file:///docs/readme.md",
name: "README",
description: "Project README documentation",
mimeType: "text/markdown",
},
{
uri: "file:///config/settings.json",
name: "Settings",
description: "Application settings",
mimeType: "application/json",
},
],
};
});
// Handle resource reads
server.setRequestHandler(ReadResourceRequestSchema, async (request) => {
const uri = request.params.uri;
if (uri.startsWith("file://")) {
const filePath = uri.replace("file://", "");
const content = await fs.readFile(filePath, "utf-8");
return {
contents: [
{
uri,
mimeType: "text/plain",
text: content,
},
],
};
}
throw new Error(`Unknown resource: ${uri}`);
});
Python Resource Example from mcp.types import Resource, ResourceContent
@app.list_resources()
async def list_resources() -> list[Resource]:
return [
Resource(
uri="file:///docs/readme.md",
name="README",
description="Project README",
mimeType="text/markdown",
),
Resource(
uri="file:///config/settings.json",
name="Settings",
description="App settings",
mimeType="application/json",
),
]
@app.read_resource()
async def read_resource(uri: str) -> ResourceContent:
if uri.startswith("file://"):
file_path = uri.replace("file://", "")
with open(file_path, "r") as f:
content = f.read()
return ResourceContent(uri=uri, mimeType="text/plain", text=content)
raise ValueError(f"Unknown resource: {uri}")
Prompts - Reusable Templates Prompts are templates that LLMs can use with arguments.
TypeScript Prompt Example import {
ListPromptsRequestSchema,
GetPromptRequestSchema,
} from "@modelcontextprotocol/sdk/types.js";
// List prompts
server.setRequestHandler(ListPromptsRequestSchema, async () => {
return {
prompts: [
{
name: "code_review",
description: "Review code for best practices",
arguments: [
{
name: "language",
description: "Programming language",
required: true,
},
{
name: "code",
description: "Code to review",
required: true,
},
],
},
],
};
});
// Handle prompt requests
server.setRequestHandler(GetPromptRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
if (name === "code_review") {
const language = args?.language || "unknown";
const code = args?.code || "";
return {
messages: [
{
role: "user",
content: {
type: "text",
text: `Review this ${language} code for best practices, security issues, and improvements:\n\n\`\`\`${language}\n${code}\n\`\`\``,
},
},
],
};
}
throw new Error(`Unknown prompt: ${name}`);
});
Python Prompt Example from mcp.types import Prompt, PromptMessage, PromptArgument
@app.list_prompts()
async def list_prompts() -> list[Prompt]:
return [
Prompt(
name="code_review",
description="Review code for best practices",
arguments=[
PromptArgument(
name="language", description="Programming language", required=True
),
PromptArgument(name="code", description="Code to review", required=True),
],
)
]
@app.get_prompt()
async def get_prompt(name: str, arguments: dict) -> list[PromptMessage]:
if name == "code_review":
language = arguments.get("language", "unknown")
code = arguments.get("code", "")
return [
PromptMessage(
role="user",
content={
"type": "text",
"text": f"Review this {language} code:\n\n```{language}\n{code}\n```",
},
)
]
raise ValueError(f"Unknown prompt: {name}")
Advanced Patterns
1. Error Handling // TypeScript
server.setRequestHandler(CallToolRequestSchema, async (request) => {
try {
const { name, arguments: args } = request.params;
if (name === "risky_operation") {
// Validate inputs
if (!args.required_param) {
throw new Error("Missing required parameter: required_param");
}
// Perform operation with proper error handling
const result = await performRiskyOperation(args.required_param);
return {
content: [{ type: "text", text: JSON.stringify(result) }],
};
}
} catch (error) {
// Return error to LLM with helpful message
return {
content: [
{
type: "text",
text: `Error: ${error instanceof Error ? error.message : String(error)}`,
},
],
isError: true,
};
}
});
# Python
@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
try:
if name == "risky_operation":
if "required_param" not in arguments:
raise ValueError("Missing required parameter: required_param")
result = await perform_risky_operation(arguments["required_param"])
return [TextContent(type="text", text=json.dumps(result))]
except Exception as e:
return [TextContent(type="text", text=f"Error: {str(e)}", isError=True)]
2. Async Operations // TypeScript - Async API calls
import axios from "axios";
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
if (name === "fetch_api_data") {
const url = args.url as string;
const response = await axios.get(url);
return {
content: [
{
type: "text",
text: JSON.stringify(response.data, null, 2),
},
],
};
}
});
# Python - Async database queries
import aiosqlite
@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
if name == "query_database":
query = arguments["query"]
async with aiosqlite.connect("database.db") as db:
async with db.execute(query) as cursor:
rows = await cursor.fetchall()
result = json.dumps(rows)
return [TextContent(type="text", text=result)]
3. Environment Variables & Configuration // TypeScript - Load config from environment
import dotenv from "dotenv";
dotenv.config();
const API_KEY = process.env.API_KEY;
const BASE_URL = process.env.BASE_URL || "https://api.example.com";
server.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name === "api_call") {
const response = await fetch(`${BASE_URL}/endpoint`, {
headers: { Authorization: `Bearer ${API_KEY}` },
});
// ...
}
});
# Python - Load config from environment
import os
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv("API_KEY")
BASE_URL = os.getenv("BASE_URL", "https://api.example.com")
@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
if name == "api_call":
headers = {"Authorization": f"Bearer {API_KEY}"}
async with aiohttp.ClientSession() as session:
async with session.get(f"{BASE_URL}/endpoint", headers=headers) as resp:
data = await resp.json()
return [TextContent(type="text", text=json.dumps(data))]
4. Streaming Responses (Large Data) // TypeScript - Stream large file contents
server.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name === "read_large_file") {
const filePath = request.params.arguments.path as string;
const stream = fs.createReadStream(filePath, { encoding: "utf-8" });
let content = "";
for await (const chunk of stream) {
content += chunk;
// Could yield chunks incrementally in future MCP versions
}
return {
content: [{ type: "text", text: content }],
};
}
});
5. Dynamic Tool Registration // TypeScript - Register tools from config
interface ToolConfig {
name: string;
description: string;
schema: object;
handler: (args: any) => Promise<any>;
}
const toolRegistry = new Map<string, ToolConfig>();
function registerTool(config: ToolConfig) {
toolRegistry.set(config.name, config);
}
// Register custom tools
registerTool({
name: "custom_tool",
description: "Dynamically registered tool",
schema: {
type: "object",
properties: {
input: { type: "string" },
},
},
handler: async (args) => {
return { result: `Processed: ${args.input}` };
},
});
// List tools dynamically
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: Array.from(toolRegistry.values()).map((tool) => ({
name: tool.name,
description: tool.description,
inputSchema: tool.schema,
})),
};
});
// Call tools dynamically
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const tool = toolRegistry.get(request.params.name);
if (!tool) {
throw new Error(`Unknown tool: ${request.params.name}`);
}
const result = await tool.handler(request.params.arguments);
return {
content: [{ type: "text", text: JSON.stringify(result) }],
};
});
Transport Types
1. STDIO (Local Execution) Default for Claude Desktop integration. Server runs as subprocess.
// Claude Desktop config
{
"mcpServers": {
"my-server": {
"command": "node",
"args": ["/path/to/server/build/index.js"],
"env": {
"API_KEY": "your-api-key" # pragma: allowlist secret
}
}
}
}
2. SSE (Server-Sent Events) For long-running servers with HTTP transport.
// TypeScript SSE server
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { SSEServerTransport } from "@modelcontextprotocol/sdk/server/sse.js";
import express from "express";
const app = express();
const server = new Server(/* ... */);
app.get("/sse", async (req, res) => {
const transport = new SSEServerTransport("/messages", res);
await server.connect(transport);
});
app.post("/messages", async (req, res) => {
// Handle incoming messages
});
app.listen(3000, () => {
console.log("MCP server listening on http://localhost:3000");
});
// Claude Desktop config for SSE
{
"mcpServers": {
"remote-server": {
"url": "http://localhost:3000/sse"
}
}
}
3. HTTP (Streamable HTTP) For REST-style MCP servers.
# Python HTTP server with FastAPI
from fastapi import FastAPI
from mcp.server.fastapi import create_fastapi_app
app = FastAPI()
mcp_app = Server("http-server")
# Define tools/resources/prompts as usual
# ...
# Mount MCP routes
app.mount("/mcp", create_fastapi_app(mcp_app))
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
Debugging MCP Servers
1. Server Logs // TypeScript - Console.error for logs (STDOUT is for MCP protocol)
server.setRequestHandler(CallToolRequestSchema, async (request) => {
console.error(`Tool called: ${request.params.name}`);
console.error(`Arguments: ${JSON.stringify(request.params.arguments)}`);
// ...
});
# Python - Use logging module
import logging
logging.basicConfig(level=logging.DEBUG, filename="mcp-server.log")
logger = logging.getLogger(__name__)
@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
logger.debug(f"Tool called: {name}")
logger.debug(f"Arguments: {arguments}")
# ...
2. MCP Inspector # Install MCP Inspector (browser-based debugging tool)
npm install -g @modelcontextprotocol/inspector
# Run inspector with your server
mcp-inspector node /path/to/server/build/index.js
# Opens browser at http://localhost:6274
# - Test tools manually
# - See request/response payloads
# - Debug JSON schema validation
3. Claude Desktop Logs # macOS
tail -f ~/Library/Logs/Claude/mcp*.log
# Windows
# Check %APPDATA%/Claude/logs/
4. Test Server Standalone // TypeScript - Add test harness
if (process.argv.includes("--test")) {
// Simulate tool call
const testRequest = {
params: {
name: "read_file",
arguments: { path: "./test.txt" },
},
};
server
.setRequestHandler(CallToolRequestSchema, async (request) => {
// ... your handler
})
.then((handler) => handler(testRequest))
.then((result) => console.log(JSON.stringify(result, null, 2)))
.catch((error) => console.error(error));
} else {
// Normal server startup
main();
}
# Test without Claude Desktop
node build/index.js --test
Real-World Server Examples
1. GitHub Integration Server // github-server/src/index.ts
import { Octokit } from "@octokit/rest";
const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN });
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: "list_repos",
description: "List repositories for a user/org",
inputSchema: {
type: "object",
properties: {
owner: { type: "string", description: "Username or org name" },
},
required: ["owner"],
},
},
{
name: "create_issue",
description: "Create a GitHub issue",
inputSchema: {
type: "object",
properties: {
owner: { type: "string" },
repo: { type: "string" },
title: { type: "string" },
body: { type: "string" },
},
required: ["owner", "repo", "title"],
},
},
],
};
});
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const { name, arguments: args } = request.params;
if (name === "list_repos") {
const { data } = await octokit.repos.listForUser({
username: args.owner as string,
});
const repos = data.map((r) => ({
name: r.name,
description: r.description,
stars: r.stargazers_count,
}));
return {
content: [{ type: "text", text: JSON.stringify(repos, null, 2) }],
};
}
if (name === "create_issue") {
const { data } = await octokit.issues.create({
owner: args.owner as string,
repo: args.repo as string,
title: args.title as string,
body: args.body as string,
});
return {
content: [
{ type: "text", text: `Issue created: ${data.html_url}` },
],
};
}
});
2. Database Query Server # database-server/server.py
import asyncpg
import json
from mcp.server import Server
from mcp.types import Tool, TextContent
app = Server("database-server")
async def get_db_pool():
return await asyncpg.create_pool(
host="localhost",
database="mydb",
user="user",
password="password" # pragma: allowlist secret
)
@app.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="query",
description="Execute SQL query (SELECT only)",
inputSchema={
"type": "object",
"properties": {
"sql": {"type": "string", "description": "SQL query"},
},
"required": ["sql"],
},
),
Tool(
name="list_tables",
description="List all tables in database",
inputSchema={"type": "object", "properties": {}},
),
]
@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
pool = await get_db_pool()
if name == "query":
sql = arguments["sql"]
# Security: Only allow SELECT
if not sql.strip().upper().startswith("SELECT"):
raise ValueError("Only SELECT queries allowed")
async with pool.acquire() as conn:
rows = await conn.fetch(sql)
result = [dict(row) for row in rows]
return [TextContent(type="text", text=json.dumps(result, indent=2))]
elif name == "list_tables":
async with pool.acquire() as conn:
tables = await conn.fetch("""
SELECT table_name FROM information_schema.tables
WHERE table_schema = 'public'
""")
result = [row["table_name"] for row in tables]
return [TextContent(type="text", text=json.dumps(result, indent=2))]
3. Web Scraper Server // scraper-server/src/index.ts
import * as cheerio from "cheerio";
import axios from "axios";
server.setRequestHandler(ListToolsRequestSchema, async () => {
return {
tools: [
{
name: "scrape_url",
description: "Scrape content from a URL",
inputSchema: {
type: "object",
properties: {
url: { type: "string", description: "URL to scrape" },
selector: {
type: "string",
description: "CSS selector for content",
},
},
required: ["url"],
},
},
],
};
});
server.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name === "scrape_url") {
const url = request.params.arguments.url as string;
const selector = request.params.arguments.selector as string;
const response = await axios.get(url);
const $ = cheerio.load(response.data);
let content: string;
if (selector) {
content = $(selector).text();
} else {
content = $("body").text();
}
return {
content: [
{
type: "text",
text: content.trim(),
},
],
};
}
});
Best Practices
1. Tool Design
Use clear, descriptive tool names (read_file not rf)
Provide detailed descriptions for LLM understanding
Define comprehensive JSON schemas with descriptions
Return structured data (JSON) when possible
Handle errors gracefully with helpful messages
Create overly broad tools (split complex operations)
Return massive payloads (paginate large datasets)
Use ambiguous parameter names
Assume LLM knows your domain-specific terminology
2. Security
Validate ALL inputs (type, range, format)
Sanitize file paths (prevent directory traversal)
Use allowlists for commands/operations
Never expose sensitive credentials in responses
Implement rate limiting for expensive operations
Use read-only access by default
// Example: Path validation
function validatePath(inputPath: string): string {
const normalized = path.normalize(inputPath);
const allowed = path.resolve("/safe/directory");
if (!normalized.startsWith(allowed)) {
throw new Error("Path outside allowed directory");
}
return normalized;
}
3. Performance
Cache expensive operations
Stream large responses when possible
Use pagination for list operations
Set reasonable timeouts
Implement request queuing for rate-limited APIs
4. Testing # Use MCP Inspector for manual testing
mcp-inspector node build/index.js
# Unit test tool handlers
npm test
# Integration test with Claude Desktop
# 1. Add to config
# 2. Restart Claude
# 3. Test in conversation
5. Documentation
Document all tools in code comments
Provide example usage in descriptions
Include error scenarios in documentation
Maintain a CHANGELOG for server updates
Common Pitfalls ❌ Writing to STDOUT (breaks MCP protocol):
// WRONG
console.log("Debug message"); // STDOUT is for MCP protocol
// CORRECT
console.error("Debug message"); // STDERR for logs
// WRONG - unhandled promise rejection
server.setRequestHandler(CallToolRequestSchema, async (request) => {
const data = await riskyOperation(); // Can throw
return { content: [{ type: "text", text: data }] };
});
// CORRECT
server.setRequestHandler(CallToolRequestSchema, async (request) => {
try {
const data = await riskyOperation();
return { content: [{ type: "text", text: data }] };
} catch (error) {
return {
content: [{ type: "text", text: `Error: ${error.message}` }],
isError: true,
};
}
});
# WRONG - synchronous file read blocks event loop
@app.call_tool()
async def call_tool(name: str, arguments: dict):
with open("large_file.txt", "r") as f: # Blocks!
content = f.read()
return [TextContent(type="text", text=content)]
# CORRECT - async file I/O
@app.call_tool()
async def call_tool(name: str, arguments: dict):
async with aiofiles.open("large_file.txt", "r") as f:
content = await f.read()
return [TextContent(type="text", text=content)]
❌ Missing required fields in schema :
// WRONG - missing "required" field
{
name: "search",
inputSchema: {
type: "object",
properties: {
query: { type: "string" }
}
// Missing: required: ["query"]
}
}
Resources
Related Skills When building MCP servers, consider these complementary skills:
typescript-core : TypeScript type safety, tsconfig optimization, and advanced patternsasyncio : Python async patterns for MCP servers with async/awaitopenrouter : Alternative LLM API integration for multi-model support
Quick TypeScript MCP Patterns (Inlined for Standalone Use) // Type-safe MCP server with TypeScript
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import {
CallToolRequestSchema,
ListToolsRequestSchema,
} from '@modelcontextprotocol/sdk/types.js';
import { z } from 'zod';
// Define tool schemas with runtime validation
const SearchSchema = z.object({
query: z.string().min(1).max(500),
limit: z.number().int().min(1).max(100).default(10),
});
type SearchArgs = z.infer<typeof SearchSchema>;
class TypeSafeMCPServer {
private server: Server;
constructor() {
this.server = new Server(
{
name: 'typed-search-server',
version: '1.0.0',
},
{
capabilities: {
tools: {},
},
}
);
this.setupToolHandlers();
}
private setupToolHandlers() {
// List tools with full type inference
this.server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [
{
name: 'search',
description: 'Search with type-safe parameters',
inputSchema: {
type: 'object',
properties: {
query: { type: 'string', minLength: 1, maxLength: 500 },
limit: { type: 'number', minimum: 1, maximum: 100, default: 10 },
},
required: ['query'],
},
},
],
}));
// Type-safe tool execution
this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
if (request.params.name === 'search') {
// Runtime validation with Zod
const args = SearchSchema.parse(request.params.arguments);
// Type-safe implementation
const results = await this.performSearch(args);
return {
content: [{ type: 'text', text: JSON.stringify(results) }],
};
}
throw new Error(`Unknown tool: ${request.params.name}`);
});
}
private async performSearch(args: SearchArgs): Promise<Array<{ title: string; url: string }>> {
// Implementation with full type safety
// args.query is string, args.limit is number
return [];
}
async run() {
const transport = new StdioServerTransport();
await this.server.connect(transport);
}
}
// Start server
const server = new TypeSafeMCPServer();
server.run();
Quick Python Async MCP Patterns (Inlined for Standalone Use) # Async MCP server with Python
import asyncio
import logging
from typing import Any
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
from pydantic import BaseModel, Field
# Type-safe argument models with Pydantic
class SearchArgs(BaseModel):
query: str = Field(..., min_length=1, max_length=500)
limit: int = Field(10, ge=1, le=100)
class AsyncMCPServer:
def __init__(self):
self.server = Server("async-search-server")
self.setup_handlers()
def setup_handlers(self):
@self.server.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="search",
description="Search with async processing",
inputSchema={
"type": "object",
"properties": {
"query": {"type": "string", "minLength": 1, "maxLength": 500},
"limit": {"type": "number", "minimum": 1, "maximum": 100, "default": 10},
},
"required": ["query"],
},
)
]
@self.server.call_tool()
async def call_tool(name: str, arguments: dict[str, Any]) -> list[TextContent]:
if name == "search":
# Validate with Pydantic
args = SearchArgs(**arguments)
# Async implementation
results = await self.perform_search(args)
return [
TextContent(
type="text",
text=str(results)
)
]
raise ValueError(f"Unknown tool: {name}")
async def perform_search(self, args: SearchArgs) -> list[dict[str, str]]:
"""Async search implementation"""
# Simulate async I/O
await asyncio.sleep(0.1)
# Use validated args (args.query is str, args.limit is int)
return [
{"title": f"Result for {args.query}", "url": "https://example.com"}
]
async def main():
"""Run async MCP server"""
logging.basicConfig(level=logging.INFO)
server = AsyncMCPServer()
# Use stdio transport for Claude Desktop
async with stdio_server() as (read_stream, write_stream):
await server.server.run(
read_stream,
write_stream,
server.server.create_initialization_options()
)
if __name__ == "__main__":
asyncio.run(main())
Quick Multi-Language MCP Patterns (Inlined for Standalone Use) TypeScript vs Python Trade-offs:
Feature TypeScript Python Type Safety Compile-time + runtime (Zod) Runtime only (Pydantic) Performance Faster startup, Node.js overhead Slower startup, better for CPU tasks Async Support Native async/await, event loop asyncio, great for I/O Ecosystem npm packages, frontend tools Data science, ML libraries Best For Web APIs, real-time tools Data processing, ML integration
Common Patterns Across Both:
Input Validation
TypeScript: Zod schemas
Python: Pydantic models
Error Handling
Both: Try/catch with specific error types
Return error content in MCP response
Resource Management
TypeScript: async/await with try/finally
Python: async context managers
Testing
TypeScript: Vitest/Jest with mock transport
Python: pytest with pytest-asyncio
Choosing Implementation Language:
// TypeScript - Best for:
// - File system operations
// - Web scraping/HTTP requests
// - JSON/API manipulation
// - Real-time data streams
// Python - Best for:
// - Data analysis (pandas, numpy)
// - Machine learning (scikit-learn, torch)
// - Database ETL operations
// - Scientific computing
[Full TypeScript, Python async, and OpenRouter patterns available in respective skills if deployed together]
Local MCP Integration Patterns (Your Repos)
.mcp.json Layout (Project-Scoped) {
"mcpServers": {
"kuzu-memory": {
"type": "stdio",
"command": "kuzu-memory",
"args": ["mcp"]
},
"mcp-vector-search": {
"type": "stdio",
"command": "uv",
"args": ["run", "mcp-vector-search", "mcp"],
"env": {
"MCP_ENABLE_FILE_WATCHING": "true"
}
}
}
}
CLI Conventions
Provide a mcp subcommand for stdio servers.
Use setup for end-to-end init + integration (mcp-vector-search, kuzu-memory).
Use install / uninstall to target specific clients (mcp-ticketer).
Provide doctor commands to validate dependencies.
Environment Conventions
File watching: MCP_ENABLE_FILE_WATCHING=true
Kuzu memory: KUZU_MEMORY_PROJECT_ROOT, KUZU_MEMORY_DB
Ticketer adapters: MCP_TICKETER_ADAPTER, GITHUB_TOKEN, GITHUB_OWNER, GITHUB_REPO
Common Server Names kuzu-memory, mcp-vector-search, mcp-ticketer, mcp-skillset, mcp-browser
Summary
MCP enables AI-native server development with tools, resources, and promptsTools are functions LLMs can call (read files, query APIs, run operations)Resources provide read-only data access (files, databases, documentation)Prompts are reusable templates with arguments for common tasksSDKs available for TypeScript and Python with full async supportClaude Desktop integration via JSON config (STDIO transport)Debugging with MCP Inspector, server logs, and standalone testingSecurity critical: validate inputs, sanitize paths, use allowlistsBest practices : Clear naming, comprehensive schemas, error handling, performance optimization 