mcp-builder skill
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Is the mcp-builder skill safe?
Clean: nothing in its files matched our rules. We read 10 files in the folder on 2026-09-28.
No findings.
Install the mcp-builder skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/guanyang/open-agent-hub.git /tmp/open-agent-hub mkdir -p ~/.claude/skills cp -r /tmp/open-agent-hub/skills/mcp-builder ~/.claude/skills/mcp-builder
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
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., githubcreateissue, githublistrepos) 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):
- TypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
- ⚡ TypeScript Guide - TypeScript patterns and examples
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 - Project structure, package.json, tsconfig.json
- 🐍 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 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 pycompile yourserver.py
- Test with MCP Inspector
See language-specific guides for detailed testing approaches and quality checklists.
Phase 4: Create Evaluations
More skills from guanyang/open-agent-hub
- Aacademy-guideStop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
- Aadvanced-evaluationThis skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
- Aalgorithmic-artCreating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
- Abaoyu-article-illustratorAnalyzes article structure, identifies positions requiring visual aids, generates illustrations with Type × Style × Palette three-dimension approach. Use when user asks to "illustrate article", "add images", "generate images for article", or "为文章配图".
- Abaoyu-comicKnowledge comic creator supporting multiple art styles and tones. Creates original educational comics with detailed panel layouts and batch-capable image generation. Use when user asks to create "知识漫画", "教育漫画", "biography comic", "tutorial comic", or "Logicomix-style comic".
- Abaoyu-compress-imageCompresses images to WebP (default) or PNG with automatic tool selection. Use when user asks to "compress image", "optimize image", "convert to webp", or reduce image file size.
- Abaoyu-cover-imageGenerates article cover images with 5 dimensions (type, palette, rendering, text, mood) combining 11 color palettes and 7 rendering styles. Supports cinematic (2.35:1), widescreen (16:9), and square (1:1) aspects. Use when user asks to "generate cover image", "create article cover", or "make cover".
- Abaoyu-danger-gemini-webGenerates images and text via reverse-engineered Gemini Web API. Supports text generation, image generation from prompts, reference images for vision input, and multi-turn conversations. Use when other skills need image generation backend, or when user requests "generate image with Gemini", "Gemini text generation", or needs vision-capable AI generation.
- Abaoyu-danger-x-to-markdownConverts X (Twitter) tweets and articles to markdown with YAML front matter. Uses reverse-engineered API requiring user consent. Use when user mentions "X to markdown", "tweet to markdown", "save tweet", or provides x.com/twitter.com URLs for conversion.
- Abaoyu-diagramCreate professional, dark-themed SVG diagrams of any type — architecture diagrams, flowcharts, sequence diagrams, structural diagrams, mind maps, timelines, illustrative/conceptual diagrams, and more. Use this skill whenever the user asks for any kind of technical or conceptual diagram, visualization of a system, process flow, data flow, component relationship, network topology, decision tree, org chart, state machine, or any visual representation of structure/logic/process. Also trigger when the user says "画个图" "画一个架构图" "diagram" "flowchart" "sequence diagram" "draw me a ..." or uploads content and asks to visualize it. Output is always a standalone .svg file.
- Abaoyu-electron-extractExtracts resources and JavaScript from any installed Electron app (`.asar` bundle), restoring original sources from `.js.map` files when available or formatting minified code with Prettier otherwise. Use when user wants to "extract Electron app", "decompile Electron", "get the source code of <app>", "inspect app.asar", "看 Electron 应用源码", "提取 .asar", or asks how a desktop Electron app is built. Skips `node_modules` and supports both macOS and Windows.
- Abaoyu-format-markdownFormats plain text or markdown files with frontmatter, titles, summaries, headings, bold, lists, and code blocks. Use when user asks to "format markdown", "beautify article", "add formatting", or improve article layout. Outputs to {filename}-formatted.md.