MCP Builder
AnthropicOfficialPublished by Anthropic
- Skill
- Tool integration
- Software development
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
mcp-builder provides a structured, four-phase workflow for developing robust MCP servers. It covers the entire lifecycle from initial research and planning to implementation, testing, and the creation of standardized evaluations. The guide emphasizes best practices for tool design, schema definition using Zod or Pydantic, and proper error handling to ensure LLMs can effectively interact with external APIs.
What it does
- Outlines a four-phase development process for MCP servers.
- Provides guidance on selecting between TypeScript (v2) and Python SDKs.
- Details best practices for tool naming, discoverability, and context management.
- Explains how to implement structured tool inputs and outputs.
- Includes instructions for testing servers with the MCP Inspector.
- Defines a standardized XML format for creating LLM-based evaluations.
When to use it
- When you need to build a new MCP server to connect an external API to an LLM.
- When you are unsure whether to use the TypeScript or Python SDK for your project.
- When you need to migrate an existing MCP server from v1 to v2.
- When you need to create a test suite to verify your MCP server's performance.
Advantages
- Promotes consistent, high-quality tool design across different languages.
- Provides clear migration paths for existing SDK versions.
- Encourages the creation of verifiable, complex evaluation scenarios.
- Focuses on actionable error messages to improve agent reliability.
Usage examples
- “How do I start building a new MCP server in TypeScript?”
- The agent will guide you through setting up a project using the v2 TypeScript SDK, including project structure and tool registration patterns.
- “What are the best practices for naming tools in an MCP server?”
- The agent will explain the importance of descriptive, action-oriented names and consistent prefixes like github_create_issue.
- “How can I verify that my MCP server is working correctly?”
- The agent will provide instructions on using the MCP Inspector and creating a 10-question evaluation suite in the required XML format.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/mcp-builder/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip mcp-builder.zip -d .agents/skills
Documentation by Anthropic
MCP Server Development Guide
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:
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 - Core guidelines
For TypeScript (recommended):
The TypeScript SDK has two major versions. Pick one before writing code:
v2 (
@modelcontextprotocol/server): the current stable release. Use it for new servers.v1 (
@modelcontextprotocol/sdk): receives bug and security fixes only. Use it only when the project'spackage.jsonalready depends on@modelcontextprotocol/sdk, or migrate that project withnpx @modelcontextprotocol/codemod@latest v1-to-v2 .
If the project already uses v1, tell the user once, in a sentence or two, that v2 (@modelcontextprotocol/server) is the current stable release, that v1 receives only bug and security fixes, and that npx @modelcontextprotocol/codemod@latest v1-to-v2 . migrates the project. Then continue with v1 unless the user asks to migrate.
Don't mix the two in one project: v1 code samples (imports from @modelcontextprotocol/sdk/...) don't apply to v2.
TypeScript SDK (v2): Use WebFetch to load
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md; the full docs index ishttps://ts.sdk.modelcontextprotocol.io/v2/llms.txt⚡ TypeScript Guide (v2) - TypeScript patterns and examples
For v1 projects only:
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/v1.x/README.mdand the TypeScript Guide (v1)
For Python:
Python SDK: Use WebFetch to load
https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md🐍 Python Guide - 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:
⚡ TypeScript Guide (v2) - Project structure, package.json, tsconfig.json (v1 for existing v1 projects)
🐍 Python Guide - 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
outputSchemawhere possible for structured dataUse
structuredContentin 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/falsedestructiveHint: true/falseidempotentHint: true/falseopenWorldHint: 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 buildto verify compilationv2 projects: run the self-check script from the v2 guide against the built server
Test with MCP Inspector:
npx @modelcontextprotocol/inspector
Python:
Verify syntax:
python -m py_compile your_server.pyTest 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 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:
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
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
📚 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.mdsuffix📋 MCP Best Practices - 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.mdTypeScript SDK (v2): Fetch from
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md; full docs index athttps://ts.sdk.modelcontextprotocol.io/v2/llms.txtTypeScript SDK (v1, existing v1 projects only): Fetch from
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/v1.x/README.md
Language-Specific Implementation Guides (Load During Phase 2)
🐍 Python Implementation Guide - Complete Python/FastMCP guide with:
Server initialization patterns
Pydantic model examples
Tool registration with
@mcp.toolComplete working examples
Quality checklist
⚡ TypeScript Implementation Guide (SDK v2) - Complete TypeScript guide for
@modelcontextprotocol/serverwith:Project setup and the server factory
Tool design: Zod 4 schemas, output formats, pagination, errors
A complete example serving stdio and Streamable HTTP, plus bearer auth
Resources, prompts, and notifications
A self-check script to verify the built server
Links to SDK examples for advanced features
A v1 → v2 translation table, migration steps, and a quality checklist
⚡ TypeScript Implementation Guide (SDK v1) - The equivalent guide for existing
@modelcontextprotocol/sdkprojects
Evaluation Guide (Load During Phase 4)
✅ Evaluation Guide - 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
Files
- ATTRIBUTION.mdAdded by AutoGineer865 B
- LICENSE.txt11.1 KiB
- SKILL.md10.9 KiB
- reference/evaluation.md21.2 KiB
- reference/mcp_best_practices.md7.2 KiB
- reference/node_mcp_server.md28.1 KiB
- reference/node_mcp_server_v2.md34.3 KiB
- reference/python_mcp_server.md24.5 KiB
- scripts/connections.pyScript4.8 KiB
- scripts/evaluation.pyScript12.3 KiB
- scripts/example_evaluation.xml1.2 KiB
- scripts/requirements.txt29 B
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Copyright 2026 Anthropic, PBC.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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limitations under the License.Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Names Claude Code tools, Needs an MCP server, Needs network access, Needs Python, Needs Node.js
Security
What our scanner noticed· Low risk
- Instructions that use the network
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/skills · skills/mcp-builder · commit 2c5958e
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Anthropic.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download mcp-builder.zip