mcp-builder
Install this skill
npx skills add anthropics/skillsWorks across Claude Code, Cursor, Codex, Copilot & Antigravity
The mcp-builder skill provides an end-to-end framework for developing Model Context Protocol (MCP) servers. It focuses on translating external APIs into functional tools that LLMs can reliably execute. The process emphasizes a clear separation between granular API endpoints and high-level workflow tools to ensure agents maintain operational flexibility. By enforcing strict schemas via Zod or Pydantic, the builder ensures that tool inputs and outputs remain predictable for the host application. It promotes defensive programming through explicit error messaging and metadata annotations, such as idempotency hints, which guide agents in safely performing state-changing operations. Developers use this skill to transform raw service documentation into standardized interfaces that permit agents to interact with third-party software, databases, or local filesystems without frequent manual intervention or complex configuration overrides.
When to Use This Skill
- β’Exposing an internal database as a set of queryable tools
- β’Connecting LLMs to proprietary ticketing systems like Jira or Linear
- β’Bridging filesystem operations to allow local file management
- β’Creating custom search tools for private documentation silos
How to Invoke This Skill
Example prompts that trigger this skill in Claude Code, Cursor, or Antigravity:
- βCreate a new MCP server for the internal billing API
- βDefine a Zod schema for the new file search tool
- βHelp me scaffold a TypeScript MCP project for github interactions
- βAdd pagination support to my existing MCP tool
- βDraft the tool descriptions for my new server to ensure the agent understands them
Pro Tips
- π‘Prioritize comprehensive API coverage over overly specialized workflow tools when starting, as this gives agents maximum flexibility to compose operations.
- π‘Adopt consistent, action-oriented tool naming conventions (e.g., `service_action_object`) to enhance discoverability and reduce agent confusion.
- π‘Craft concise yet informative tool descriptions and parameter schemas to optimize context management for the LLM.
What this skill does
- β’Generates structured tool input schemas using Zod or Pydantic
- β’Implements standard MCP transport interfaces for stdio or HTTP
- β’Configures tool metadata including idempotency and destructive action hints
- β’Standardizes error messaging to provide actionable feedback to LLMs
- β’Integrates pagination logic for large data retrieval
When not to use it
- βWhen a direct API integration via a simple function call suffices
- βWhen real-time low-latency streaming between tools is required
- βIf the external service lacks a stable, documented API
Example workflow
- Analyze the target API documentation to identify necessary endpoints
- Initialize the server using the appropriate SDK for TypeScript or Python
- Define tool input schemas and implement core data-fetching functions
- Add metadata annotations for safety and idempotency
- Test tool execution using an MCP-compatible client
- Refine tool descriptions based on agent performance logs
Prerequisites
- βKnowledge of TypeScript or Python
- βTarget service API documentation
- βMCP SDK installed in the project environment
Pitfalls & limitations
- !Writing ambiguous tool names that confuse the agent
- !Failing to handle pagination, causing timeouts with large datasets
- !Overloading the agent with too many specific tools instead of functional abstractions
- !Neglecting output schema definitions that prevent structured data parsing
FAQ
How it compares
Unlike writing standalone API wrappers, mcp-builder enforces a standardized protocol structure, ensuring that your tools are immediately discoverable and usable by any MCP-compliant client without custom integration code.
π Full skill instructions β original source: anthropics/skills
## 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
## π 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 clientβsome 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.xmlThen 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**: [π 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- [β‘ 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- [π 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:
- [β‘ TypeScript Guide](./reference/node_mcp_server.md) - Project structure, package.json, tsconfig.json
- [π 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 [β 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:
<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
## π 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- [π 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)
- [π 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
- [β‘ 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)
- [β 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
How to Use This Skill Unit
Option A: Project-Specific (Recommended)
- Click "Download" above
- In your project, create the directory:
.agent/skills/mcp-builder/ - Save the file as
SKILL.md - The agent will automatically discover the skill based on its description.
Option B: Global Installation (All Agents)
Save the file to these locations to make it available across all projects:
- Claude Code:
~/.claude/skills/anthropics/skills/mcp-builder/SKILL.md - Cursor:
~/.cursor/skills/anthropics/skills/mcp-builder/SKILL.md - Antigravity:
~/.gemini/antigravity/skills/anthropics/skills/mcp-builder/SKILL.md
π Install with CLI:npx skills add anthropics/skills