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mcp-builder
Use when building MCP servers or integrating external APIs/services via MCP (TypeScript or Python).
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
メニュー
Use when building MCP servers or integrating external APIs/services via MCP (TypeScript or Python).
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Systematic analysis and reasoning workflows. Use when performing audits, investigation, requirements analysis, risk analysis, scenario/edge-case enumeration, or design analysis that demands evidence-based, comprehensive coverage.
Code quality and safety standards. Activates when editing or reviewing code. Use for quality priorities, safety checks, and code correctness verification.
Use when performing coding tasks (implement features, fix bugs, refactor code). Orchestrates language detection, mode switching (normal/autopilot/full-auto), quality enforcement, and verification.
Break a problem into atomic, testable subproblems. Use when facing complex tasks, unclear requirements, or planning implementation strategy.
Start structured feature implementation workflow. Use when implementing new features, adding functionality, or building complete user-facing capabilities.
Use when starting work that needs isolated branches or parallel feature development using git worktrees.
| name | mcp-builder |
| description | Use when building MCP servers or integrating external APIs/services via MCP (TypeScript or Python). |
| allowed-tools | ["shell","read_file","write_file","apply_patch","update_plan"] |
| metadata | {"short-description":"Build MCP servers and tools"} |
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 real-world tasks.
API Coverage vs. Workflow Tools: Balance comprehensive endpoint coverage with specialized workflow tools. When uncertain, prefer comprehensive coverage.
Tool Naming and Discoverability: Use clear, consistent, action-oriented tool names.
Context Management: Keep tool descriptions concise; support filtering/pagination.
Actionable Error Messages: Errors should guide agents toward resolution with specific next steps.
Start with the sitemap and fetch markdown pages using web.run:
https://modelcontextprotocol.io/sitemap.xmlhttps://modelcontextprotocol.io/specification/draft.mdReview architecture, transports, tool/resource/prompt definitions.
Recommended stack:
Load framework documentation:
reference/mcp_best_practices.mdhttps://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.mdreference/node_mcp_server.mdhttps://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.mdreference/python_mcp_server.mdSee language-specific guides in reference/.
Input Schema: Zod (TS) or Pydantic (Python) with constraints and examples
Output Schema: Define outputSchema where possible; return structured content
Annotations:
readOnlyHint, destructiveHint, idempotentHint, openWorldHintTypeScript:
npm run buildnpx @modelcontextprotocol/inspectorPython:
python -m py_compile your_server.pyLoad reference/evaluation.md and create 10 realistic, stable, read-only evaluation questions with verifiable answers.
These files are bundled with this skill under the local reference/ directory:
reference/mcp_best_practices.mdreference/node_mcp_server.mdreference/python_mcp_server.mdreference/evaluation.mdTarget: ${ARGUMENTS}