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.
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.
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 at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix for markdown format.
Key pages to review:
- Specification overview and architecture
- Transport mechanisms (streamable HTTP, stdio)
- Tool, resource, and prompt definitions
#### 1.3 Plan Your Implementation
Understand the API:
Review the service's API documentation to identify key endpoints, authentication requirements, and data models.
Tool Selection:
Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.
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Phase 2: Implementation
#### 2.1 Set Up Project Structure
Recommended stack:
- Language: TypeScript (high-quality SDK support)
- Transport: Streamable HTTP for remote servers, stdio for local servers
#### 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
Tool Description:
- Concise summary of functionality
- Parameter descriptions
- Return type schema
Annotations:
readOnlyHint: true/falsedestructiveHint: true/falseidempotentHint: true/falseopenWorldHint: true/false
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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
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Phase 4: Create Evaluations
After implementing your MCP server, create comprehensive evaluations to test its effectiveness.
#### 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
- Tool Inspection: List available tools and understand their capabilities
- Content Exploration: Use READ-ONLY operations to explore available data
- Question Generation: Create 10 complex, realistic questions
- 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