A comprehensive development workflow for building, testing, and evaluating high-quality Model Context Protocol (MCP) servers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mcp
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install mcp using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is MCP Builder?
The MCP Builder is a specialized technical skill designed to guide developers through the creation of high-quality Model Context Protocol (MCP) servers. By utilizing Openclaw Skills, this resource enables LLMs to interact effectively with external services through well-designed tools. It emphasizes agent-centric design, focusing on workflows rather than simple API wrapping to ensure that AI agents can accomplish complex, real-world tasks efficiently.
This skill provides a structured methodology for both Python and Node/TypeScript implementations, ensuring that every server built with Openclaw Skills follows industry best practices. From managing constrained context windows to designing actionable error messages, the MCP Builder ensures that your integrations are stable, scalable, and highly performant for any AI agent environment.
MCP Builder Use Cases
- Building custom MCP servers to integrate internal company APIs with AI agents.
- Developing standardized toolsets for LLMs using Openclaw Skills to ensure consistent performance.
- Implementing complex data retrieval workflows that require pagination and character limit management.
- Creating rigorous evaluation harnesses to test how well an AI agent utilizes specific tools.
How MCP Builder Works
- Research and Planning: Use Openclaw Skills to analyze the latest MCP protocol documentation and map out high-impact workflows.
- Architecture Design: Define input validation models using Pydantic or Zod and design shared utilities for error handling and response formatting.
- Systematic Implementation: Register tools using the appropriate SDK while applying language-specific best practices for Python or TypeScript.
- Quality Review: Audit the implementation for DRY principles, type safety, and documentation completeness.
- Evaluation: Generate an XML-based evaluation suite to verify that the agent can solve complex, realistic problems using the new server.
MCP Builder Setup
To start building with this skill, ensure you have your development environment ready for either Python or TypeScript.
For Python implementations using Openclaw Skills:
pip install mcp
For Node/TypeScript implementations:
npm install @modelcontextprotocol/sdk
After installation, fetch the latest protocol specifications using the integrated WebFetch tools provided within the MCP Builder workflow.
MCP Builder Data Schema & Taxonomy
The MCP Builder skill organizes data through a rigorous schema-first approach to ensure compatibility across Openclaw Skills.
| Data Layer | Specification | Purpose |
|---|---|---|
| Tool Inputs | Pydantic (Python) / Zod (TS) | Ensures strict validation and provides clear metadata for LLM discovery. |
| Tool Outputs | JSON / Markdown | Provides structured data or human-readable summaries based on agent needs. |
| Error Handling | Natural Language | Returns actionable feedback to guide the agent toward correct usage. |
| Evaluation | XML QA Pairs | Defines independent, verifiable test cases for server performance. |
MCP Builder Advanced Features
- Context Window Optimization: Strategies to truncate data and make every token count within agent sessions.
- Workflow Consolidation: Patterns for combining multiple API calls into single, high-impact tool operations.
- Evaluation-Driven Development: A dedicated phase for creating 10+ complex QA pairs to ensure reliability.
- Protocol Hinting: Support for advanced annotations like readOnlyHint and idempotentHint to improve agent reasoning logic.
- Language-Specific Best Practices: Tailored checklists for both Python FastMCP and TypeScript SDK environments integrated into Openclaw Skills.
SKILL.md
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