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).
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.
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# 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.
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>
```
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# 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)