latestaiagents/mcp-server-authoring

MCP Server Authoring

Build MCP servers in TypeScript or Python that any MCP-compatible client can consume.

When to Use

• You want to expose an internal API or tool to Claude Desktop / Claude Code / Cursor
• You're building a shareable MCP server (npm, PyPI, or Smithery registry)
• You need to wrap read-only data sources as MCP resources
• You want agents to invoke your service without custom glue code per client

Core Model

An MCP server exposes three primitives:

| Primitive | Purpose | Side effects | |---|---|---| | Tool | Action the agent calls | May have side effects (writes, network calls) | | Resource | Data the agent reads | Read-only, addressed by URI | | Prompt | Reusable prompt template | No side effects; user-invocable |

Pick the right primitive — exposing read operations as tools wastes context when they should be resources.

Quickstart — TypeScript (stdio)

npm init -y
npm i @modelcontextprotocol/sdk zod
npm i -D typescript tsx @types/node

// src/server.ts
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: "weather-server",
  version: "1.0.0",
});

server.tool(
  "get_forecast",
  "Get a weather forecast for a location",
  {
    location: z.string().describe("City name or lat,lng"),
    days: z.number().int().min(1).max(14).default(3),
  },
  async ({ location, days }) => {
    const data = await fetchForecast(location, days);
    return {
      content: [{ type: "text", text: JSON.stringify(data) }],
    };
  },
);

server.resource(
  "station",
  "weather://stations/{id}",
  async (uri) => {
    const id = uri.pathname.split("/").pop()!;
    const station = await fetchStation(id);
    return {
      contents: [{ uri: uri.href, mimeType: "application/json", text: JSON.stringify(station) }],
    };
  },
);

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

Run it and register in the client's mcp.json:

{
  "mcpServers": {
    "weather": {
      "command": "npx",
      "args": ["-y", "tsx", "src/server.ts"]
    }
  }
}

Quickstart — Python (stdio)

uv add "mcp[cli]"

# server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("weather-server")

@mcp.tool()
async def get_forecast(location: str, days: int = 3) -> dict:
    """Get a weather forecast for a location."""
    return await fetch_forecast(location, days)

@mcp.resource("weather://stations/{id}")
async def station(id: str) -> dict:
    return await fetch_station(id)

if __name__ == "__main__":
    mcp.run()

Tool Contract Rules

1. Name: snake_case, verb-led (search_issues, not IssueSearcher)
2. Description: one sentence, starts with a verb, mentions key args
3. Schema: use Zod / Pydantic with descriptive .describe() on every field
4. Return shape: always { content: [{ type: "text", text: ... }] } — wrap JSON as text
5. Errors: throw — the SDK converts to isError: true. Include remediation in the message
6. Idempotency: write tools should accept an idempotency_key when possible

Progressive Disclosure Pattern

Don't dump 40 tools on the client. Expose a small stable surface and let tools return follow-up handles:

server.tool("list_projects", "List accessible projects", {}, async () => {
  const projects = await api.listProjects();
  return {
    content: [{
      type: "text",
      text: `Found ${projects.length} projects. Use get_project with id to read one:\n` +
            projects.map(p => `- ${p.id}: ${p.name}`).join("\n"),
    }],
  };
});

Testing

Use the MCP Inspector during development:

npx @modelcontextprotocol/inspector tsx src/server.ts

It gives you a UI to call every tool, inspect every resource, and replay prompts. Run it in CI against a mock backend to catch schema regressions.

Publishing

• npm: publish the server as a bin entry so users run npx your-mcp-server
• Smithery registry: add smithery.yaml with config schema for one-click install
• Private: ship as a Docker image and point command at docker run

Best Practices

1. Keep your tool surface ≤ 15 tools — more hurts model selection accuracy
2. Return text, not binary — for large payloads use resources with URIs
3. Stream long-running tools via server.sendNotification progress events
4. Version your server's name suffix (github-v2) when making breaking changes
5. Never read env vars for secrets at tool-call time — load at startup and fail fast
6. Log every tool invocation server-side — agents behave unpredictably, you'll need the audit trail