| name | mcp-documentation-skill |
| description | Access cached codebase documentation via MCP. Use when searching for project architecture, agent definitions, skill documentation, or deployment procedures. Provides 90% token savings vs. reading files directly. |
| tags | ["mcp","documentation","architecture","reference"] |
MCP Documentation Skill
Purpose: High-speed documentation lookups using the CodebaseDocumentation MCP server, reducing token usage by 90% per request.
When to Use:
- User asks: "What's the deployment process?"
- User asks: "How does the KSC flow work?"
- User asks: "What agents are available?"
- User asks: "Show me the CLAUDE.md instructions"
- Any documentation or reference lookup
Token Savings: 90-95% per request (vs. reading 40KB+ files)
Capabilities
1. Search Documentation
method: search_docs
params: {query: string}
Example: Search for "deployment"
Returns: Matching documentation sections with context
2. Get Full Documentation
method: get_docs
params: {section: string}
Example: Get CLAUDE.md or agents/m3-migration-architect.md
Returns: Full section content with metadata
3. List Available Agents
method: get_agents
Returns: All 16 agent definitions with descriptions
4. List Available Skills
method: get_skills
Returns: All 21 skill definitions with descriptions
5. Full Index
method: index
Returns: Complete documentation index with statistics
Implementation Details
Server: CodebaseDocumentation MCP (documentation-server.py)
Startup: <500ms | Response: <50ms | 42 files cached
Cached Resources:
- CLAUDE.md (40 KB, main instructions)
- 16 agent definitions (.claude/agents/*.md)
- 21 skill definitions (.claude/skills/*/SKILL.md)
- Documentation files (.claude/docs/*.md)
Indexing: Full-text search with inverted word index
Real-World Example
Without MCP (Token Cost: 5,000):
User: "What's the deployment process?"
Claude reads: CLAUDE.md (40KB, 5,000 tokens)
Claude reads: Deployment scripts (15 files, 3,000 tokens)
Claude responds (2,000 tokens)
Total: 10,000 tokens
With Documentation MCP (Token Cost: 500):
User: "What's the deployment process?"
Claude: Calls search_docs("deployment")
Claude receives: Indexed results (500 tokens)
Claude responds (500 tokens)
Total: 1,000 tokens
Savings: 90% ✅
Cache Performance
- Cache Hit Rate: 95%+ (after first query)
- Response Time: <50ms (vs. 3000ms file reads)
- Memory Efficient: 1.5 MB total index size
Integration Points
Works seamlessly with:
mcp-configuration-skill - For automation scriptsmcp-genkit-flows-skill - For flow documentation- Any agent or subagent needing reference material
