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Creating mcp servers

Skill oaustegard/claude-skills/plugins/development-tools/skills/creating-mcp-servers

My collection of Claude skills

Install
npx -y skills add oaustegard/claude-skills --skill creating-mcp-servers

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What its author says it does

Copied from the file, not written here

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.

SKILL.md

5.5 KB, ~1.3k tokens by cl100k_base, as published. Nobody here has run it

Creating MCP Servers

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

Core Capabilities

  1. Apply mandatory patterns - Four critical 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)

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:

  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 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):

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

Gives 0 of the 12 instructions most mcp tooling skills give in ~1.3k tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-06

  • create ten complex read-only evaluation questionsin 71 of 638, across 17 files
  • test servers using MCP Inspectorin 60 of 638, across 18 files
  • provide actionable error messagesin 56 of 638, across 14 files
  • prioritize comprehensive API coverage over specific workflowsin 54 of 638, across 12 files
  • use TypeScript and Streamable HTTP for remote serversin 53 of 638, across 7 files
  • define structured output schemas where possiblein 51 of 638, across 9 files
  • use Zod or Pydantic for input schemasin 48 of 638, across 6 files
  • fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • load framework documentation using WebFetchin 45 of 638, across 3 files
  • verify each evaluation answer independentlyin 45 of 638, across 3 files
  • implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 28 of 638, across 10 files

Said here and by no other author read

  • install dependencies using uv
  • fetch documentation using web_fetch
  • consult documentation index for relevant urls
  • apply mandatory patterns to all implementations
  • use annotated parameters with field
  • use single-sentence docstrings

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.