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AI-SKILLS

MCP Builder

👤 Anthropics
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📅 January 19, 2026
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Introduction

The MCP Builder is an essential resource for developers looking to extend the capabilities of Large Language Models through the Model Context Protocol (MCP). This skill provides a structured framework for creating robust MCP servers that allow agents to interact with external APIs, databases, and local services with high precision.

By following the MCP Builder methodology, you can transform static API documentation into dynamic, tool-enabled environments. Whether you are using TypeScript or Python, this guide ensures your tools are discoverable, reliable, and optimized for agentic workflows.

Key Features:

  • Standardized Workflow: A 4-phase process covering research, implementation, testing, and evaluation.
  • Multi-Language Support: Best practices for both the Node/TypeScript SDK and Python (FastMCP).
  • Schema-First Design: Guidance on using Zod and Pydantic for strict input/output validation.
  • Evaluation Framework: A rigorous method to verify tool effectiveness using complex, real-world test cases.

Installation

To utilize the MCP Builder guidelines within your development environment, you can reference the documentation directly or configure your local environment to follow the MCP specification.

If you are using this as part of a Claude Code environment, ensure you have the necessary SDKs installed for your preferred language:

For TypeScript:

Terminal window
npm install @modelcontextprotocol/sdk zod

For Python:

Terminal window
pip install mcp pydantic

Usage

The MCP Builder guides you through the lifecycle of tool development. You can interact with this skill to generate boilerplate code, refine tool descriptions, or create evaluation XML files.

Example: Planning a Tool

When building a new integration, focus on Action-Oriented Naming. Instead of a generic name like get_data, use specific prefixes:

  • github_create_issue
  • stripe_list_customers
  • slack_post_message

Example: Tool Implementation (TypeScript)

Define clear schemas to help the model understand the required parameters:

const CreateIssueSchema = z.object({
  owner: z.string().describe("The account owner of the repository"),
  repo: z.string().describe("The name of the repository"),
  title: z.string().describe("The title of the issue"),
  body: z.string().optional().describe("The contents of the issue"),
});

Development Phases

1. Research and Planning

Before writing code, identify the balance between API Coverage and Specialized Workflow Tools. High-quality servers prioritize comprehensive coverage to give agents the flexibility to compose complex operations.

2. Implementation

Implement core infrastructure including authentication, error handling, and pagination. Use readOnlyHint, destructiveHint, and idempotentHint to provide the LLM with safety context regarding the tool’s behavior.

3. Review and Test

Use the MCP Inspector to manually verify tool execution.

  • TypeScript: npx @modelcontextprotocol/inspector
  • Python: Verify syntax and run the server in a local environment to test transport.

4. Comprehensive Evaluation

The final stage involves creating 10 complex, realistic questions to test your server. These questions should be:

  • Verifiable: Have a clear, single answer.
  • Read-only: Use non-destructive operations.
  • Independent: Do not rely on previous question states.

Tip: Always provide actionable error messages. Instead of “404 Not Found,” suggest “The repository ‘X’ was not found. Please verify the name and try again.”

Reference Documentation

For deeper dives into specific implementation details, refer to the following resources:

  • Protocol Specification: https://modelcontextprotocol.io
  • TypeScript SDK: Available on GitHub at modelcontextprotocol/typescript-sdk
  • Python SDK: Available on GitHub at modelcontextprotocol/python-sdk