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mcp-builder

Guide MCP server design and implementation with evaluation-driven workflows.

112|26|Updated Jan 25, 2026
Search Tags:#error handling#mcp#server development#schema validation#tool design#llm tools#evaluation harness
One-click install
npx skills add https://github.com/Wide-Moat/open-computer-use --skill mcp-builder-wide-moat
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/Wide-Moat/open-computer-use/tree/main/skills/examples/mcp-builder
Command: npx skills add https://github.com/Wide-Moat/open-computer-use --skill mcp-builder-wide-moat

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill reduces the risk and guesswork involved in building Model Context Protocol (MCP) servers by providing a thorough, evaluation-driven blueprint for tool design, schemas, error handling, and testing.

Core Features & Use Cases

  • Workflow-first tool design: Build tools that help agents complete real tasks end-to-end instead of just wrapping individual API endpoints.
  • LLM-friendly interfaces: Emphasize constrained context usage, clear tool naming, actionable error messages, and response formatting for efficient agent reasoning.
  • Protocol-accurate implementation planning: Guides you through MCP spec review, SDK documentation study (Python and TypeScript), and implementation checklists.
  • Quality-focused delivery: Covers safe testing approaches and a structured path to create high-signal evaluations that verify real agent performance.

Quick Start

Use this guidance to design your MCP server’s tools and validations, then create an evaluation XML file and run the evaluation harness to verify the model can solve complex read-only questions using only your tools.

Frequently Asked Questions about mcp-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design MCP servers for real agent tasks instead of just wrapping API endpoints?▼

To design MCP servers for real agent tasks, use a workflow-first approach that builds tools to help agents complete end-to-end tasks. This involves specifying validated tool schemas and creating LLM-friendly responses for efficient reasoning.

What is the best way to test MCP server tools for LLM agent performance?▼

The best way to test MCP server tools for LLM agent performance is to create stable read-only evaluations using an evaluation harness. You design an evaluation XML file to verify the model can solve complex questions using only your tools.

How do I implement robust error handling and schema validation for MCP server development?▼

Implement robust error handling and schema validation for MCP server development by applying a protocol-accurate blueprint. This includes studying the MCP spec, reviewing SDK documentation, and following implementation checklists for actionable error messages.

Does this MCP server development workflow support both Python and TypeScript SDKs?▼

Yes, this MCP server development workflow supports both Python (FastMCP) and Node/TypeScript SDKs. It guides you through studying the respective SDK documentation and implementation planning for integrating external APIs.

Why do my LLM tools fail to complete complex tasks end-to-end?▼

LLM tools fail to complete complex tasks when they lack constrained context usage, clear naming, and actionable error messages. An evaluation-driven production workflow ensures tool design provides high-signal responses for efficient agent reasoning.

When do I need an evaluation-driven workflow for building MCP servers?▼

You need an evaluation-driven workflow for building MCP servers when reducing the risk and guesswork of tool design. It is required to verify real agent performance through stable read-only evaluations before production deployment.

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