# Mcp

> MCP (Model Context Protocol) - Build AI-native servers with tools, resources, and prompts. TypeScript/Python SDKs for Claude Desktop integration.

- **Type:** Skill
- **Install:** `agentstack add skill-bobmatnyc-claude-mpm-skills-mcp`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [bobmatnyc](https://agentstack.voostack.com/s/bobmatnyc)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [bobmatnyc](https://github.com/bobmatnyc)
- **Source:** https://github.com/bobmatnyc/claude-mpm-skills/tree/main/toolchains/ai/protocols/mcp

## Install

```sh
agentstack add skill-bobmatnyc-claude-mpm-skills-mcp
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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 tasks
- **Client-Server**: MCP servers expose capabilities, clients (like Claude Desktop) consume them
- **Transport**: STDIO (local), SSE (Server-Sent Events), HTTP (network)

**Official SDKs**:
- TypeScript: `@modelcontextprotocol/sdk`
- Python: `mcp`

**Installation**:
```bash
# 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

```bash
# 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

```typescript
// 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

```json
// ~/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

```bash
# Build TypeScript
npm run build

# Restart Claude Desktop (Cmd+Q and reopen)
# Server appears in 🔌 menu
```

## Quick Start - Python Server

### 1. Create Server

```python
# 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

```json
{
  "mcpServers": {
    "filesystem": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}
```

### 3. Test

```bash
# 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

```typescript
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

```python
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

```typescript
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

```python
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
// 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
# 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
// 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
# 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
// 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
# 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
// 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
// TypeScript - Register tools from config
interface ToolConfig {
  name: string;
  description: string;
  schema: object;
  handler: (args: any) => Promise;
}

const toolRegistry = new Map();

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

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [bobmatnyc](https://github.com/bobmatnyc)
- **Source:** [bobmatnyc/claude-mpm-skills](https://github.com/bobmatnyc/claude-mpm-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** yes
- **Filesystem access:** yes
- **Shell / process execution:** yes
- **Environment & secrets:** yes
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-bobmatnyc-claude-mpm-skills-mcp
- Seller: https://agentstack.voostack.com/s/bobmatnyc
- Browse the marketplace: https://agentstack.voostack.com/browse

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