---
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).
name: mcp-builder
---

# MCP Server Development Guide

> **💡 MCP Tool Available**: Use **Context7**, **Tavily**, **BraveSearch**, or **Serper.dev** first; only if those fail, use **WebSearch** or **WebFetch** as needed.

## 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:**

Use Context7, Tavily, BraveSearch, or Serper.dev to search the MCP specification; only if those fail, use WebSearch or WebFetch as needed. Start with the sitemap to find relevant pages, then load specific pages as needed.

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 Context7, Tavily, BraveSearch, or Serper.dev to query the TypeScript SDK documentation; only if those fail, use WebSearch or WebFetch as needed.
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns and examples

**For Python:**
- **Python SDK**: Use Context7, Tavily, BraveSearch, or Serper.dev to query the Python SDK documentation; only if those fail, use WebSearch or WebFetch as needed.
- [🐍 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 Context7, Tavily, BraveSearch, or Serper.dev first; only if those fail, use WebSearch or 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**: Use Context7, Tavily, BraveSearch, or Serper.dev to search the MCP specification; only if those fail, use WebSearch or WebFetch; load specific pages as needed
- [📋 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**: Use Context7, Tavily, BraveSearch, or Serper.dev to query the Python SDK documentation; only if those fail, use WebSearch or WebFetch
- **TypeScript SDK**: Use Context7, Tavily, BraveSearch, or Serper.dev to query the TypeScript SDK documentation; only if those fail, use WebSearch or WebFetch

### 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

# MCP Builder

> Principles for building MCP servers.

---

## 1. MCP Overview

### What is MCP?

Model Context Protocol - standard for connecting AI systems with external tools and data sources.

### Core Concepts

| Concept | Purpose |
|---------|---------|
| **Tools** | Functions AI can call |
| **Resources** | Data AI can read |
| **Prompts** | Pre-defined prompt templates |

---

## 2. Server Architecture

### Project Structure

```
my-mcp-server/
├── src/
│   └── index.ts      # Main entry
├── package.json
└── tsconfig.json
```

### Transport Types

| Type | Use |
|------|-----|
| **Stdio** | Local, CLI-based |
| **SSE** | Web-based, streaming |
| **WebSocket** | Real-time, bidirectional |

---

## 3. Tool Design Principles

### Good Tool Design

| Principle | Description |
|-----------|-------------|
| Clear name | Action-oriented (get_weather, create_user) |
| Single purpose | One thing well |
| Validated input | Schema with types and descriptions |
| Structured output | Predictable response format |

### Input Schema Design

| Field | Required? |
|-------|-----------|
| Type | Yes - object |
| Properties | Define each param |
| Required | List mandatory params |
| Description | Human-readable |

---

## 4. Resource Patterns

### Resource Types

| Type | Use |
|------|-----|
| Static | Fixed data (config, docs) |
| Dynamic | Generated on request |
| Template | URI with parameters |

### URI Patterns

| Pattern | Example |
|---------|---------|
| Fixed | `docs://readme` |
| Parameterized | `users://{userId}` |
| Collection | `files://project/*` |

---

## 5. Error Handling

### Error Types

| Situation | Response |
|-----------|----------|
| Invalid params | Validation error message |
| Not found | Clear "not found" |
| Server error | Generic error, log details |

### Best Practices

- Return structured errors
- Don't expose internal details
- Log for debugging
- Provide actionable messages

---

## 6. Multimodal Handling

### Supported Types

| Type | Encoding |
|------|----------|
| Text | Plain text |
| Images | Base64 + MIME type |
| Files | Base64 + MIME type |

---

## 7. Security Principles

### Input Validation

- Validate all tool inputs
- Sanitize user-provided data
- Limit resource access

### API Keys

- Use environment variables
- Don't log secrets
- Validate permissions

---

## 8. Configuration

### Desktop app MCP config

| Field | Purpose |
|-------|---------|
| command | Executable to run |
| args | Command arguments |
| env | Environment variables |

---

## 9. Testing

### Test Categories

| Type | Focus |
|------|-------|
| Unit | Tool logic |
| Integration | Full server |
| Contract | Schema validation |

---

## 10. Best Practices Checklist

- [ ] Clear, action-oriented tool names
- [ ] Complete input schemas with descriptions
- [ ] Structured JSON output
- [ ] Error handling for all cases
- [ ] Input validation
- [ ] Environment-based configuration
- [ ] Logging for debugging

---

> **Remember:** MCP tools should be simple, focused, and well-documented. The AI relies on descriptions to use them correctly.