Agent skill
creating-mcp-servers
Creates production-ready MCP servers using FastMCP v2. Use when building MCP servers, optimizing tool descriptions for context efficiency, implementing progressive disclosure for multiple capabilities, or packaging servers for distribution.
Install this agent skill to your Project
npx add-skill https://github.com/oaustegard/claude-skills/tree/main/creating-mcp-servers
Metadata
Additional technical details for this skill
- version
- 1.1.0
SKILL.md
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
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
hello-demo
Delivers a static Hello World HTML demo page with bookmarklet. Use when user requests the hello demo, hello world demo, or demo page.
installing-skills
Install skills from github.com/oaustegard/claude-skills into /mnt/skills/user. Use when user mentions "install skills", "load skills", "add skills", "update skills", "refresh skills", or references a skill not currently installed.
extracting-keywords
Extract keywords from documents using YAKE algorithm with support for 34 languages (Arabic to Chinese). Use when users request keyword extraction, key terms, topic identification, content summarization, or document analysis. Includes domain-specific stopwords for AI/ML and life sciences. Optional deeper extraction mode (n=2+n=3 combined) for comprehensive coverage.
remembering
Advanced memory operations reference. Basic patterns (profile loading, simple recall/remember) are in project instructions. Consult this skill for background writes, memory versioning, complex queries, edge cases, session scoping, retention management, type-safe results, proactive memory hints, GitHub access detection, autonomous curation, episodic scoring, and decision traces.
orchestrating-agents
Orchestrates parallel API instances, delegated sub-tasks, and multi-agent workflows with streaming and tool-enabled delegation patterns. Use for parallel analysis, multi-perspective reviews, or complex task decomposition.
check-tools
Validates development tool installations across Python, Node.js, Java, Go, Rust, C/C++, Git, and system utilities. Use when verifying environments or troubleshooting dependencies.
Didn't find tool you were looking for?