mcp-builder – skill Agent & Tự động hóa cho agent AI
Tạo máy chủ MCP (Model Context Protocol) giúp LLM tương tác với dịch vụ bên ngoài thông qua các công cụ được thiết kế tốt. Chất lượng máy chủ MCP được đo bằng mức độ nó giúp LLM hoàn thành tác vụ thực tế.
Cài skill mcp-builder bằng một lệnh
Chọn công cụ bạn đang dùng, copy lệnh và dán vào terminal. Skill sẽ nằm trong thư mục skills của công cụ đó và sẵn sàng dùng ở phiên làm việc tiếp theo.
Claude Code
mkdir -p ~/.claude/skills && curl -fsSL https://skill.dn1nguoi.com/z/mcp-builder-e473b.tgz | tar xz -C ~/.claude/skills
Gọi trong Claude Code: /mcp-builder – rồi mô tả công việc bạn muốn làm.
Codex
mkdir -p ~/.codex/skills && curl -fsSL https://skill.dn1nguoi.com/z/mcp-builder-e473b.tgz | tar xz -C ~/.codex/skills
Gọi trong Codex: $mcp-builder – rồi mô tả công việc bạn muốn làm.
Hermes Agent
mkdir -p ~/.hermes/skills && curl -fsSL https://skill.dn1nguoi.com/z/mcp-builder-e473b.tgz | tar xz -C ~/.hermes/skills
Gọi trong Hermes Agent: /mcp-builder – rồi mô tả công việc bạn muốn làm.
DeepSeek
mkdir -p ~/.deepcode/skills && curl -fsSL https://skill.dn1nguoi.com/z/mcp-builder-e473b.tgz | tar xz -C ~/.deepcode/skills
Gọi trong DeepSeek: /mcp-builder – rồi mô tả công việc bạn muốn làm.
Nội dung SKILL.md
Nội dung SKILL.md gốc bằng tiếng Anh.
MCP Server Development Guide
Modified in AAS on 2026-09-05: version-scoped examples and bounded evaluation.
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. Start with the minimum operations needed for the user's authorized workflow. Expand coverage from observed gaps.
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/2025-11-25/index).
Key pages to review:
- Specification overview and architecture
- Transport mechanisms (streamable HTTP, stdio)
- Tool, resource, and prompt definitions
1.3 Study Framework Documentation
Choose the project-compatible stack:
- Language: TypeScript or Python, according to the existing runtime and tested client
- 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](./reference/mcp_best_practices.md) - Core guidelines
For TypeScript (recommended):
- TypeScript SDK: Use an available read-only browser or HTTP tool to load
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md - [⚡ TypeScript Guide](./reference/node_mcp_server.md) - TypeScript patterns and examples
For Python:
- Python SDK: Use an available read-only browser or HTTP tool to load
https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md - [🐍 Python Guide](./reference/python_mcp_server.md) - 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 available read-only research tools as needed.
Tool Selection:
List the exact operations and permissions needed by the task. Keep write tools separate and require authorization at the server boundary.
---
Phase 2: Implementation
2.1 Set Up Project Structure
See language-specific guides for project setup:
- [⚡ TypeScript Guide](./reference/node_mcp_server.md) - Project structure, package.json, tsconfig.json
- [🐍 Python Guide](./reference/python_mcp_server.md) - 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 data - Use
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 compilation - Test with MCP Inspector:
npx @modelcontextprotocol/inspector
Python:
- Verify syntax:
python -m py_compile your_server.py - Test 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](./reference/evaluation.md) 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:
1. Tool Inspection: List available tools and understand their capabilities
2. Content Exploration: Use READ-ONLY operations to explore available data
3. Question Generation: Create 10 complex, realistic questions
4. 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](./reference/mcp_best_practices.md) - 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.md - TypeScript SDK: Fetch from
https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
Language-Specific Implementation Guides (Load During Phase 2)
- [🐍 Python Implementation Guide](./reference/python_mcp_server.md) - Complete Python/FastMCP guide with:
- Server initialization patterns
- Pydantic model examples
- Tool registration with
@mcp.tool - Integration sketches with explicit prerequisites
- Quality checklist
- [⚡ TypeScript Implementation Guide](./reference/node_mcp_server.md) - Complete TypeScript guide with:
- Project structure
- Zod schema patterns
- Tool registration with
server.registerTool - Integration sketches with explicit prerequisites
- Quality checklist
Evaluation Guide (Load During Phase 4)
- [✅ Evaluation Guide](./reference/evaluation.md) - 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
When to Use
Use for a new MCP tool contract, a transport/client compatibility defect, or a review
of a server's bounded input/ou
… (nội dung đầy đủ có trong gói tải về)
Skill liên quan
- mcp-builder-ms – Dùng skill này khi xây dựng máy chủ MCP để tích hợp API hoặc dịch vụ bên ngoài, bằng…
- mcp-dependency-drift-audit – Kiểm tra tĩnh cấu hình MCP để tìm tham chiếu gói npm/npx có thể thay đổi, trước khi phê…
- mcp-server-security – Bảo mật máy chủ Model Context Protocol (MCP) với mã hóa đường truyền, phân quyền công…
- memory-handoff – Bàn giao phiên làm việc linh hoạt giữa các CLI của agent: đọc file handoff của dự án khi…
- memory-safety-patterns – Các mẫu lập trình an toàn bộ nhớ đa ngôn ngữ, bao gồm RAII, ownership, smart pointer và…
- memory-systems – Thiết kế kiến trúc bộ nhớ ngắn hạn, dài hạn và dựa trên đồ thị. Dùng khi xây dựng agent…
- mercury-mcp – Bảng tra cứu nhanh cho các công cụ MCP của Mercury (proton). Dùng khi kết nối với máy…
- mesh-memory – Bộ nhớ ngữ nghĩa tự lưu trữ cho AI agent qua MCP. Lưu worklog, quyết định và ghi chú…