mcp-builder

分类: 开发与编程 | 上传者: ToxicPine | 下载: 0 | 版本: v1.0（最新）
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
更新日志: Source: GitHub https://github.com/ToxicPine/ambit
目录结构

当前层级: .agents/skills/mcp-builder/
📁 reference/
- 📄 evaluation.md 21.3 KB
- 📄 mcp_best_practices.md 7.4 KB
- 📄 node_mcp_server.md 28.1 KB
- 📄 python_mcp_server.md 24.6 KB
📁 scripts/
- 📄 connections.py 4.8 KB
- 📄 evaluation.py 12.3 KB
- 📄 example_evaluation.xml 1.2 KB
- 📄 requirements.txt 29 B
📄 LICENSE.txt 11.1 KB
📄 SKILL.md 8.9 KB
SKILL.md

---
name: mcp-builder
description: Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
license: Complete terms in LICENSE.txt
---

# MCP Server Development Guide

## Overview

Create MCP (Model Context Protocol) servers that enable LLMs to interact with
external services through well-designed tools. The quality of an MCP server is
measured by how well it enables LLMs to accomplish real-world tasks.

---

# Process

## 🚀 High-Level Workflow

Creating a high-quality MCP server involves four main phases:

### Phase 1: Deep Research and Planning

#### 1.1 Understand Modern MCP Design

**API Coverage vs. Workflow Tools:** Balance comprehensive API endpoint coverage
with specialized workflow tools. Workflow tools can be more convenient for
specific tasks, while comprehensive coverage gives agents flexibility to compose
operations. Performance varies by client—some clients benefit from code
execution that combines basic tools, while others work better with higher-level
workflows. When uncertain, prioritize comprehensive API coverage.

**Tool Naming and Discoverability:** Clear, descriptive tool names help agents
find the right tools quickly. Use consistent prefixes (e.g.,
`github_create_issue`, `github_list_repos`) and action-oriented naming.

**Context Management:** Agents benefit from concise tool descriptions and the
ability to filter/paginate results. Design tools that return focused, relevant
data. Some clients support code execution which can help agents filter and
process data efficiently.

**Actionable Error Messages:** Error messages should guide agents toward
solutions with specific suggestions and next steps.

#### 1.2 Study MCP Protocol Documentation

**Navigate the MCP specification:**

Start with the sitemap to find relevant pages:
`https://modelcontextprotocol.io/sitemap.xml`

Then fetch specific pages with `.md` suffix for markdown format (e.g.,
`https://modelcontextprotocol.io/specification/draft.md`).

Key pages to review:

- Specification overview and architecture
- Transport mechanisms (streamable HTTP, stdio)
- Tool, resource, and prompt definitions

#### 1.3 Study Framework Documentation

**Recommended stack:**

- **Language**: TypeScript (high-quality SDK support and good compatibility in
  many execution environments e.g. MCPB. Plus AI models are good at generating
  TypeScript code, benefiting from its broad usage, static typing and good
  linting tools)
- **Transport**: Streamable HTTP for remote servers, using stateless JSON
  (simpler to scale and maintain, as opposed to stateful sessions and streaming
  responses). stdio for local servers.

**Load framework documentation:**

- **MCP Best Practices**:
  [📋 View Best Practices](./reference/mcp_best_practices.md) - Core guidelines

**For TypeScript (recommended):**

- **TypeScript SDK**: Use WebFetch to load
  `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md`
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns
  and examples

**For Python:**

- **Python SDK**: Use WebFetch to load
  `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
- [🐍 Python Guide](./reference/python_mcp_server.md) - Python patterns and
  examples

#### 1.4 Plan Your Implementation

**Understand the API:** Review the service's API documentation to identify key
endpoints, authentication requirements, and data models. Use web search and
WebFetch as needed.

**Tool Selection:** Prioritize comprehensive API coverage. List endpoints to
implement, starting with the most common operations.

---

### Phase 2: Implementation

#### 2.1 Set Up Project Structure

See language-specific guides for project setup:

- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - Project structure,
  package.json, tsconfig.json
- [🐍 Python Guide](./reference/python_mcp_server.md) - Module organization,
  dependencies

#### 2.2 Implement Core Infrastructure

Create shared utilities:

- API client with authentication
- Error handling helpers
- Response formatting (JSON/Markdown)
- Pagination support

#### 2.3 Implement Tools

For each tool:

**Input Schema:**

- Use Zod (TypeScript) or Pydantic (Python)
- Include constraints and clear descriptions
- Add examples in field descriptions

**Output Schema:**

- Define `outputSchema` where possible for structured data
- Use `structuredContent` in tool responses (TypeScript SDK feature)
- Helps clients understand and process tool outputs

**Tool Description:**

- Concise summary of functionality
- Parameter descriptions
- Return type schema

**Implementation:**

- Async/await for I/O operations
- Proper error handling with actionable messages
- Support pagination where applicable
- Return both text content and structured data when using modern SDKs

**Annotations:**

- `readOnlyHint`: true/false
- `destructiveHint`: true/false
- `idempotentHint`: true/false
- `openWorldHint`: true/false

---

### Phase 3: Review and Test

#### 3.1 Code Quality

Review for:

- No duplicated code (DRY principle)
- Consistent error handling
- Full type coverage
- Clear tool descriptions

#### 3.2 Build and Test

**TypeScript:**

- Run `npm run build` to verify compilation
- Test with MCP Inspector: `npx @modelcontextprotocol/inspector`

**Python:**

- Verify syntax: `python -m py_compile your_server.py`
- Test with MCP Inspector

See language-specific guides for detailed testing approaches and quality
checklists.

---

### Phase 4: Create Evaluations

After implementing your MCP server, create comprehensive evaluations to test its
effectiveness.

**Load [✅ Evaluation Guide](./reference/evaluation.md) for complete evaluation
guidelines.**

#### 4.1 Understand Evaluation Purpose

Use evaluations to test whether LLMs can effectively use your MCP server to
answer realistic, complex questions.

#### 4.2 Create 10 Evaluation Questions

To create effective evaluations, follow the process outlined in the evaluation
guide:

1. **Tool Inspection**: List available tools and understand their capabilities
2. **Content Exploration**: Use READ-ONLY operations to explore available data
3. **Question Generation**: Create 10 complex, realistic questions
4. **Answer Verification**: Solve each question yourself to verify answers

#### 4.3 Evaluation Requirements

Ensure each question is:

- **Independent**: Not dependent on other questions
- **Read-only**: Only non-destructive operations required
- **Complex**: Requiring multiple tool calls and deep exploration
- **Realistic**: Based on real use cases humans would care about
- **Verifiable**: Single, clear answer that can be verified by string comparison
- **Stable**: Answer won't change over time

#### 4.4 Output Format

Create an XML file with this structure:

```xml
<evaluation>
  <qa_pair>
    <question>Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?</question>
    <answer>3</answer>
  </qa_pair>
<!-- More qa_pairs... -->
</evaluation>
```

---

# Reference Files

## 📚 Documentation Library

Load these resources as needed during development:

### Core MCP Documentation (Load First)

- **MCP Protocol**: Start with sitemap at
  `https://modelcontextprotocol.io/sitemap.xml`, then fetch specific pages with
  `.md` suffix
- [📋 MCP Best Practices](./reference/mcp_best_practices.md) - Universal MCP
  guidelines including:
  - Server and tool naming conventions
  - Response format guidelines (JSON vs Markdown)
  - Pagination best practices
  - Transport selection (streamable HTTP vs stdio)
  - Security and error handling standards

### SDK Documentation (Load During Phase 1/2)

- **Python SDK**: Fetch from
  `https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md`
- **TypeScript SDK**: Fetch from
  `https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md`

### Language-Specific Implementation Guides (Load During Phase 2)

- [🐍 Python Implementation Guide](./reference/python_mcp_server.md) - Complete
  Python/FastMCP guide with:
  - Server initialization patterns
  - Pydantic model examples
  - Tool registration with `@mcp.tool`
  - Complete working examples
  - Quality checklist

- [⚡ TypeScript Implementation Guide](./reference/node_mcp_server.md) -
  Complete TypeScript guide with:
  - Project structure
  - Zod schema patterns
  - Tool registration with `server.registerTool`
  - Complete working examples
  - Quality checklist

### Evaluation Guide (Load During Phase 4)

- [✅ Evaluation Guide](./reference/evaluation.md) - Complete evaluation
  creation guide with:
  - Question creation guidelines
  - Answer verification strategies
  - XML format specifications
  - Example questions and answers
  - Running an evaluation with the provided scripts
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