Skill Seekers vs Cognee

In-depth architectural comparison of the Skill Seekers and Cognee MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

Which MCP Server Should You Choose?

Choose Skill Seekers when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).
• Primary tools included: Multi-source scraping and extraction, Codebase scanning, AI-assisted skill enhancement.

Explore Skill Seekers Details

Choose Cognee when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: API Key required (BYOK (Pay Provider Direct)).
• You have access to required keys: LLM_API_KEY.
• Primary tools included: Persistent and session-scoped memory, Graph, vector, and code-aware retrieval, Text and code ingestion.

Feature & Specification Comparison

Specification	Skill Seekers skill-seekers Knowledge & Memory	Cognee topoteretes Knowledge & Memory
Summary	Transform 17 source types (docs, GitHub repos, PDFs, videos, Jupyter, Confluence, Notion, Slack/Discord) into AI-ready skills and RAG knowledge. 35 MCP tools for scraping, packaging, enhancing, and exporting to vector databases (Weaviate, Chroma, FAISS, Qdrant). Supports 16+ target platforms.	Memory manager for AI apps and Agents using various graph and vector stores and allowing ingestion from 30+ data sources
Category & Scope

Tools & Capabilities Breakdown

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

{
  "mcpServers": {
    "skill-seekers-skill-seekers": {
      "command": "uvx",
      "args": [
        "skill-seekers"
      ]
    }
  }
}

{
  "mcpServers": {
    "topoteretes-cognee": {
      "command": "uvx",
      "args": [
        "cognee"
      ],
      "env": {
        "LLM_API_KEY": "YOUR_LLM_API_KEY_HERE"
      }
    }
  }
}

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