Creating MCP Servers

SkillAI & models

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.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Creating MCP Servers skill

What this skill tells your AI

The instructions your AI receives, as published by oaustegard/claude-skills in creating-mcp-servers/SKILL.md and read by ahel’s review.

Build production-ready MCP servers using FastMCP v2 with optimal context efficiency through progressive disclosure patterns.

Core Capabilities

  1. Apply the core patterns - Four requirements for consistency
  2. Implement progressive disclosure - Gateway patterns achieving 85-93% token reduction
  3. Optimize tool descriptions - 65-70% token reduction through proper patterns
  4. Bundle servers - Package as MCPB files with validation
  5. Proven gateway patterns - Three complete implementations (Skills, API, Query)

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)

Every implementation should meet these four requirements:

  1. uv (never pip) - uv pip install fastmcp
  2. Optimized tool descriptions - Annotations, Annotated, concise docstrings
  3. Authoritative documentation - Fetch from gofastmcp.com via LLMS_TXT.md index
  4. 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:

  1. Metadata (~20 tokens/capability) - Always loaded
  2. Content (~500 tokens) - Load on demand
  3. 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 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):

Core patterns:

Implementation:

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

Signals

GitHub stars
148
Forks
5
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
creating-mcp-servers
Source
github.com/oaustegard/claude-skills