mcp-developer

Files (6)

• SKILL.md
• protocol.md · references/
• python-sdk.md · references/
• resources.md · references/
• tools.md · references/
• typescript-sdk.md · references/

SKILL.md (first lines)

# MCP Developer

Senior MCP (Model Context Protocol) developer with deep expertise in building servers and clients that connect AI systems with external tools and data sources.

## Core Workflow

1. **Analyze requirements** — Identify data sources, tools needed, and client apps
2. **Initialize project** — `npx @modelcontextprotocol/create-server my-server` (TypeScript) or `pip install mcp` + scaffold (Python)
3. **Design protocol** — Define resource URIs, tool schemas (Zod/Pydantic), and prompt templates
4. **Implement** — Register tools and resource handlers; configure transport (stdio/SSE/HTTP)
5. **Test** — Run `npx @modelcontextprotocol/inspector` to verify protocol compliance interactively; confirm tools appear, schemas accept valid inputs, and error responses are well-formed JSON-RPC 2.0. **Feedback loop:** if schema validation fails → inspect Zod/Pydantic error output → fix schema definition → re-run inspector. If a tool call returns a malformed response → check transport serialisation → fix handler → re-test.
6. **Deploy** — Package, add auth/rate-limiting, configure env vars, monitor

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Protocol | `references/protocol.md` | Message types, lifecycle, JSON-RPC 2.0 |
| TypeScript SDK | `references/typescript-sdk.md` | Building servers/clients in Node.js |
| Python SDK | `references/python-sdk.md` | Building servers/clients in Python |
| Tools | `references/tools.md` | Tool definitions, schemas, execution |
| Resources | `references/resources.md` | Resource providers, URIs, templates |

## Minimal Working Example

### TypeScript — Tool with Zod Validation

```typescript
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({ name: "my-server", version: "1.1.0" });

// Register a tool with validated input schema
server.tool(
  "get_weather",
  "Fetch current weather for a location",
  {
    location: z.string().min(1).describe("City name or coordinates"),
    units: z.enum(["celsius", "fahrenheit"]).default("celsius"),
  },
  async ({ location, units }) => {
    // Implementation: call external API, transform response
    const data = await fetchWeather(location, units); // your fetch logic
    return {
      content: [{ type: "text", text: JSON.stringify(data) }],
    };
  }
);

// Register a resource provider
server.resource(
  "config://app",
  "Application configuration",
  async (uri) => ({
    contents: [{ uri: uri.href, text: JSON.stringify(getConfig()), mimeType: "application/json" }],
  })
);

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

### Python — Tool with Pydantic Validation

```python
from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field

mcp = FastMCP("my-server")

class WeatherInput(BaseModel):
    location: str = Field(..., min_length=1, description="City name or coordinates")
    units: str = Field("celsius", pattern="^(celsius|fahrenheit)$")

@mcp.tool()
async def get_weather(location: str, units: str = "celsius") -> str:
    """Fetch current weather for a location."""
    data = await fetch_weather(location, units)  # your fetch logic
    return str(data)

@mcp.resource("config://app")
async def app_config() -> str:
    """Expose application configuration as a resource."""
    return json.dumps(get_config())

if __name__ == "__main__":
    mcp.run()  # defaults to stdio transport
```

**Expected tool call flow:**
```
Client → { "method": "tools/call", "params": { "name": "get_weather", "arguments": { "location": "Berlin" } } }
Server → { "result": { "content": [{ "type": "text", "text": "{\"temp\": 18, \"units\": \"celsius\"}" }] } }
```

## Constraints

### MUST DO
- Implement JSON-RPC 2.0 protocol correctly
- Validate all inputs with schemas (Zod/Pydantic)
- Use proper transport mechanisms (stdio/HTTP/SSE)
- Implement comprehensive error handling
- Add authentication and authorization
- Log protocol messages for debugging
- Test protocol compliance thoroughly
- Document server capabilities

### MUST NOT DO
- Skip input validation on tool inputs
- Expose sensitive data in resource content
- Ignore protocol version compatibility
- Mix synchronous code with async transports
- Hardcode credentials or secrets
- Return unstructured errors to clients
- Deploy without rate limiting

From Vishwajeetsrk/JARVIS-AI-OS, MIT.