# Latestaiagents Agent Skills MCP Server Authoring

> MCP Server Authoring

- Skill: `tomevault-io/latestaiagents-agent-skills-mcp-server-authoring` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/latestaiagents-agent-skills-mcp-server-authoring`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/latestaiagents-agent-skills-mcp-server-authoring/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/latestaiagents-agent-skills-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)

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

```ts
// 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`:

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

## Quickstart — Python (stdio)

```bash
uv add "mcp[cli]"
```

```python
# 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:

```ts
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:

```bash
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

---
> Source: [latestaiagents/agent-skills](https://github.com/latestaiagents/agent-skills) — distributed by [TomeVault](https://tomevault.io).
<!-- tomevault:4.0:skill_md:2026-05-23 -->