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.
git clone --depth 1 https://github.com/oaustegard/claude-skills /tmp/creating-mcp-servers && cp -r /tmp/creating-mcp-servers/creating-mcp-servers ~/.claude/skills/creating-mcp-serversSKILL.md
# 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](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](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)
```bash
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](references/MCPB_BUNDLING.md) for manifest format.
## Reference Library
**Documentation index (load first for FastMCP knowledge):**
- [LLMS_TXT.md](references/LLMS_TXT.md) - Complete FastMCP v2 documentation URLs
**Core patterns:**
- [MANDATORY_PATTERNS.md](references/MANDATORY_PATTERNS.md) - Four critical requirements
- [PROGRESSIVE_DISCLOSURE.md](references/PROGRESSIVE_DISCLOSURE.md) - Architecture for 5+ capabilities
**Implementation:**
- [GATEWAY_PATTERNS.md](references/GATEWAY_PATTERNS.md) - Three production-ready implementations
- [MCPB_BUNDLING.md](references/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):**
```python
@mcp.tool()
async def search_items(query: str):
"""Search for items in the database.
This tool allows comprehensive searching..."""
```
**After (55 tokens):**
```python
@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 gatewayGitHub repository access in containerized environments using REST API and credential detection. Use when git clone fails, or when accessing private repos/writing files via API.
Securely manages API credentials for multiple providers (Anthropic Claude, Google Gemini, GitHub). Use when skills need to access stored API keys for external service invocations.
Guidance for asking clarifying questions when user requests are ambiguous, have multiple valid approaches, or require critical decisions. Use when implementation choices exist that could significantly affect outcomes.
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Browse Bluesky content via API and firehose - search posts, fetch user activity, sample trending topics, read feeds and lists, analyze and categorize accounts. Supports authenticated access for personalized feeds. Use for Bluesky research, user monitoring, trend analysis, feed reading, firehose sampling, account categorization.
Generate progressive disclosure indexes for GitHub repositories to use as Claude project knowledge. Use when setting up projects referencing external documentation, creating searchable indexes of technical blogs or knowledge bases, combining multiple repos into one index, or when user mentions "index", "github repo", "project knowledge", or "documentation reference".
Analyze and categorize Bluesky accounts by topic using keyword extraction. Use when users mention Bluesky account analysis, following/follower lists, topic discovery, account curation, or network analysis.