skill-seekers-reference/docs/advanced/mcp-server.md

MCP Server Setup Guide

Skill Seekers v3.2.0
Integrate with AI agents via Model Context Protocol

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What is MCP?

MCP (Model Context Protocol) lets AI agents like Claude Code control Skill Seekers through natural language:

You: "Scrape the React documentation"
Claude: ▶️ scrape_docs({"url": "https://react.dev/"})
        ✅ Done! Created output/react/

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Installation

# Install with MCP support
pip install skill-seekers[mcp]

# Verify
skill-seekers-mcp --version

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Transport Modes

stdio Mode (Default)

For Claude Code, VS Code + Cline:

skill-seekers-mcp

Use when:

• Running in Claude Code
• Direct integration with terminal-based agents
• Simple local setup

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HTTP Mode

For Cursor, Windsurf, HTTP clients:

# Start HTTP server
skill-seekers-mcp --transport http --port 8765

# Custom host
skill-seekers-mcp --transport http --host 0.0.0.0 --port 8765

Use when:

• IDE integration (Cursor, Windsurf)
• Remote access needed
• Multiple clients

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Claude Code Integration

Automatic Setup

# In Claude Code, run:
/claude add-mcp-server skill-seekers

Or manually add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "skill-seekers": {
      "command": "skill-seekers-mcp",
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "GITHUB_TOKEN": "ghp_..."
      }
    }
  }
}

Usage

Once connected, ask Claude:

"List available configs"
"Scrape the Django documentation"
"Package output/react for Gemini"
"Enhance output/my-skill with security-focus workflow"

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Cursor IDE Integration

Setup

1. Start MCP server:

skill-seekers-mcp --transport http --port 8765

2. In Cursor Settings → MCP:
   • Name: skill-seekers
   • URL: http://localhost:8765

Usage

In Cursor chat:

"Create a skill from the current project"
"Analyze this codebase and generate a cursorrules file"

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Windsurf Integration

Setup

1. Start MCP server:

skill-seekers-mcp --transport http --port 8765

2. In Windsurf Settings:
   • Add MCP server endpoint: http://localhost:8765

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Available Tools

27 tools organized by category:

Core Tools (9)

• list_configs - List presets
• generate_config - Create config from URL
• validate_config - Check config
• estimate_pages - Page estimation
• scrape_docs - Scrape documentation
• package_skill - Package skill
• upload_skill - Upload to platform
• enhance_skill - AI enhancement
• install_skill - Complete workflow

Extended Tools (10)

• scrape_github - GitHub repo
• scrape_pdf - PDF extraction
• scrape_generic - Generic scraper for 10 new source types (see below)
• scrape_codebase - Local code
• unified_scrape - Multi-source
• detect_patterns - Pattern detection
• extract_test_examples - Test examples
• build_how_to_guides - How-to guides
• extract_config_patterns - Config patterns
• detect_conflicts - Doc/code conflicts

Config Sources (5)

• add_config_source - Register git source
• list_config_sources - List sources
• remove_config_source - Remove source
• fetch_config - Fetch configs
• submit_config - Submit configs

Vector DB (4)

• export_to_weaviate
• export_to_chroma
• export_to_faiss
• export_to_qdrant

scrape_generic Tool

The scrape_generic tool is the generic entry point for 10 new source types added in v3.2.0. It delegates to the appropriate CLI scraper module.

Supported source types: jupyter, html, openapi, asciidoc, pptx, rss, manpage, confluence, notion, chat

Parameters:

Name	Type	Required	Description
source_type	string	Yes	One of the 10 supported source types
name	string	Yes	Skill name for the output
path	string	No	File or directory path (for file-based sources)
url	string	No	URL (for URL-based sources like confluence, notion, rss)

Usage examples:

"Scrape the Jupyter notebook analysis.ipynb"
→ scrape_generic(source_type="jupyter", name="analysis", path="analysis.ipynb")

"Extract content from the API spec"
→ scrape_generic(source_type="openapi", name="my-api", path="api-spec.yaml")

"Process the PowerPoint slides"
→ scrape_generic(source_type="pptx", name="slides", path="presentation.pptx")

"Scrape the Confluence wiki"
→ scrape_generic(source_type="confluence", name="wiki", url="https://wiki.example.com")

See MCP Reference for full details.

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Common Workflows

Workflow 1: Documentation Skill

User: "Create a skill from React docs"
Claude: ▶️ scrape_docs({"url": "https://react.dev/"})
        ⏳ Scraping...
        ✅ Created output/react/
        
        ▶️ package_skill({"skill_directory": "output/react/", "target": "claude"})
        ✅ Created output/react-claude.zip
        
        Skill ready! Upload to Claude?

Workflow 2: GitHub Analysis

User: "Analyze the facebook/react repo"
Claude: ▶️ scrape_github({"repo": "facebook/react"})
        ⏳ Analyzing...
        ✅ Created output/react/
        
        ▶️ enhance_skill({"skill_directory": "output/react/", "workflow": "architecture-comprehensive"})
        ✅ Enhanced with architecture analysis

Workflow 3: Multi-Platform Export

User: "Create Django skill for all platforms"
Claude: ▶️ scrape_docs({"config": "django"})
        ✅ Created output/django/
        
        ▶️ package_skill({"skill_directory": "output/django/", "target": "claude"})
        ▶️ package_skill({"skill_directory": "output/django/", "target": "gemini"})
        ▶️ package_skill({"skill_directory": "output/django/", "target": "openai"})
        ✅ Created packages for all platforms

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Configuration

Environment Variables

Set in ~/.claude/mcp.json or before starting server:

export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=AIza...
export OPENAI_API_KEY=sk-...
export GITHUB_TOKEN=ghp_...

Server Options

# Debug mode
skill-seekers-mcp --verbose

# Custom port
skill-seekers-mcp --port 8080

# Allow all origins (CORS)
skill-seekers-mcp --cors

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Security

Local Only (stdio)

# Only accessible by local Claude Code
skill-seekers-mcp

HTTP with Auth

# Use reverse proxy with auth
# nginx, traefik, etc.

API Key Protection

# Don't hardcode keys
# Use environment variables
# Or secret management

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Troubleshooting

"Server not found"

# Check if running
curl http://localhost:8765/health

# Restart
skill-seekers-mcp --transport http --port 8765

"Tool not available"

# Check version
skill-seekers-mcp --version

# Update
pip install --upgrade skill-seekers[mcp]

"Connection refused"

# Check port
lsof -i :8765

# Use different port
skill-seekers-mcp --port 8766

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See Also

• MCP Reference - Complete tool reference
• MCP Tools Deep Dive - Advanced usage
• MCP Protocol - Official MCP docs