creating-mcp-servers
について
このスキルは、FastMCP v2を使用して本番環境対応のMCPサーバーを開発することを支援し、コンテキスト効率のためのツール記述の最適化と段階的開示パターンの実装に重点を置いています。サーバーのバンドル機能と、トークン使用量を大幅に削減する実証済みのゲートウェイパターンを適用する機能を提供します。配布用のMCPサーバーを構築、最適化、またはパッケージ化する際にご利用ください。
クイックインストール
Claude Code
推奨/plugin add https://github.com/majiayu000/claude-skill-registrygit clone https://github.com/majiayu000/claude-skill-registry.git ~/.claude/skills/creating-mcp-serversこのコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします
ドキュメント
Creating MCP Servers
Build production-ready MCP servers using FastMCP v2 with optimal context efficiency through progressive disclosure patterns.
Core Capabilities
- Apply mandatory patterns - Four critical requirements for consistency
- Implement progressive disclosure - Gateway patterns achieving 85-93% token reduction
- Optimize tool descriptions - 65-70% token reduction through proper patterns
- Bundle servers - Package as MCPB files with validation
- Proven gateway patterns - Three complete implementations (Skills, API, Query)
Trigger Patterns
Activate this skill when:
- "MCP server", "create MCP", "build MCP", "FastMCP"
- "progressive disclosure", "gateway pattern", "context efficient"
- "optimize MCP", "reduce context", "tool descriptions"
- "MCPB", "bundle MCP", "package server"
Architecture Decision
1-3 simple tools?
→ Standard FastMCP with optimized tools
Load: references/MANDATORY_PATTERNS.md
5+ related capabilities?
→ Gateway pattern (progressive disclosure)
Load: references/PROGRESSIVE_DISCLOSURE.md
Load: references/GATEWAY_PATTERNS.md
Optimize existing server?
→ Apply mandatory patterns
Load: references/MANDATORY_PATTERNS.md
Package for distribution?
→ MCPB bundler
Load: references/MCPB_BUNDLING.md
Execute: scripts/create_mcpb.py
Need FastMCP documentation?
→ Search references/LLMS_TXT.md for relevant URLs
→ Use web_fetch on gofastmcp.com URLs
Mandatory Patterns (Summary)
Four critical requirements for ALL implementations:
- uv (never pip) -
uv pip install fastmcp - Optimized tool descriptions - Annotations, Annotated, concise docstrings
- Authoritative documentation - Fetch from gofastmcp.com via LLMS_TXT.md index
- Apply all patterns - Every implementation meets verification checklist
Details in references/MANDATORY_PATTERNS.md
Documentation Retrieval Workflow
To fetch FastMCP documentation:
1. Read references/LLMS_TXT.md - complete URL index
2. Search for relevant topic keywords
3. Use web_fetch on matched URLs (append .md for markdown)
4. Apply patterns from fetched documentation
Example: Authentication patterns → Search LLMS_TXT.md for "authentication" → web_fetch https://gofastmcp.com/servers/auth/authentication.md
Progressive Disclosure Pattern
For servers with 5+ capabilities:
Three-tier loading:
- Metadata (~20 tokens/capability) - Always loaded
- Content (~500 tokens) - Load on demand
- Execution (0 tokens) - Execute without loading
Achieves 85-93% baseline reduction. See references/PROGRESSIVE_DISCLOSURE.md
Implementation Phases
Phase 1: Research
Read LLMS_TXT.md → Find relevant URLs → web_fetch documentation
Phase 2: Implement
Load appropriate reference based on architecture decision. Apply all four mandatory patterns.
Phase 3: Package (Optional)
cd /home/claude
zip -r server-name.mcpb manifest.json server.py README.md
cp server-name.mcpb /mnt/user-data/outputs/
See references/MCPB_BUNDLING.md for manifest format.
Reference Library
Documentation index (load first for FastMCP knowledge):
- LLMS_TXT.md - Complete FastMCP v2 documentation URLs
Core patterns:
- MANDATORY_PATTERNS.md - Four critical requirements
- PROGRESSIVE_DISCLOSURE.md - Architecture for 5+ capabilities
Implementation:
- GATEWAY_PATTERNS.md - Three production-ready implementations
- MCPB_BUNDLING.md - Packaging and distribution
Scripts:
scripts/create_mcpb.py- Bundle MCP servers into .mcpb files
Verification Checklist
Before completing any FastMCP implementation:
✓ Uses uv (not pip)
✓ FastMCP docs fetched from LLMS_TXT.md URLs (not web_search)
✓ Tool annotations (readOnlyHint, title, openWorldHint)
✓ Annotated parameters with Field
✓ Single-sentence docstrings
✓ 65-70% token reduction vs verbose
✓ Server instructions concise (<100 chars)
For gateway implementations, additionally verify:
✓ 85%+ baseline context reduction
✓ Discover returns metadata only
✓ Load fetches content on demand
✓ Execute runs without context cost
Tool Description Pattern
Before (180 tokens):
@mcp.tool()
async def search_items(query: str):
"""Search for items in the database.
This tool allows comprehensive searching..."""
After (55 tokens):
@mcp.tool(
annotations={"title": "Search", "readOnlyHint": True, "openWorldHint": False}
)
async def search_items(
query: Annotated[str, Field(description="Search text")],
ctx: Context = None
):
"""Search items. Fast full-text search across all fields."""
Common Pitfalls
❌ Using mcpb pack CLI (causes crashes, just use zip)
❌ Using pip instead of uv
❌ web_search for FastMCP docs (use web_fetch on LLMS_TXT.md URLs)
❌ Verbose tool descriptions
❌ Missing tool annotations
❌ Gateway for 1-3 tools (overhead exceeds benefit)
❌ Mixing unrelated capabilities in single gateway
GitHub リポジトリ
関連スキル
content-collections
メタThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
creating-opencode-plugins
メタThis skill provides the structure and API specifications for creating OpenCode plugins that hook into 25+ event types like commands, files, and LSP operations. It offers implementation patterns for JavaScript/TypeScript modules that intercept and extend the AI assistant's lifecycle. Use it when you need to build event-driven plugins for monitoring, custom handling, or extending OpenCode's capabilities.
polymarket
メタThis skill enables developers to build applications with the Polymarket prediction markets platform, including API integration for trading and market data. It also provides real-time data streaming via WebSocket to monitor live trades and market activity. Use it for implementing trading strategies or creating tools that process live market updates.
langchain
メタLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
