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Compare MCP servers

Compare scores, permissions, risks, and fit in one decision-focused table.

DimensionHaiku RAGOpinionated agentic RAG powered by LanceDB, Pydantic AI, and DoclingDuckDuckGo Search MCP ServerMCP server providing web search and content fetching via DuckDuckGoBasic MemoryYour AI never forgets again.
FMRS40 / 100 · D74 / 100 · B72 / 100 · B
Reliability8 / 2011 / 2010 / 20
Security and permissions6 / 2015 / 2015 / 20
Maintenance9 / 2017 / 2016 / 20
Documentation9 / 2017 / 2017 / 20
Setup experience8 / 2014 / 2014 / 20
Best for
  • Local-first RAG scenarios without mandatory cloud dependencies
  • Question answering systems requiring high-quality citations
  • Research or document analysis involving figures and vision QA
  • Developers who want flexibility in choosing embedding and reranking providers
  • Developers needing quick, API-key-free web search capabilities
  • Privacy-conscious users who prefer local search execution
  • AI application development with Claude Desktop or Claude Code
  • Privacy-conscious developers who want local data storage.
  • Researchers who need AI-assisted management of large note collections.
  • Users of Markdown editors like Obsidian.
Not for
  • Fully cloud-hosted large-scale production environments (though cloud storage is supported, it's primarily local-first)
  • Simple keyword search without document structure or images
  • Enterprise users requiring official support or commercial assurance (the project is MIT-licensed community project)
  • Enterprise applications requiring high-scale, reliable search APIs
  • Users needing advanced search features like custom ranking or filters
  • Accessing sites blocked by TLS fingerprinting without installing the optional browser backend
  • Users needing cloud sync and mobile access (unless using the paid cloud service).
  • Minimalists who want zero configuration.
  • Scenarios requiring large-scale vector databases and complex retrieval (local SQLite may be insufficient).
Required permissions
  • File system access: reading and indexing local documents
  • Network access: fetching documents from HTTP, S3, etc., and calling embedding/QA provider APIs
  • Local code execution: analysis capability uses sandboxed Python execution, so this may pose security risks
  • Outbound network access to DuckDuckGo and other websites
  • Reads environment variables for SafeSearch, region, and CA certificates
  • Optionally reads a CA certificate file (for TLS-intercepting proxies)
  • Read and write to the local filesystem (default: ~/basic-memory).
  • Perform file and directory operations (create, edit, delete).
  • Access local SQLite database (or Postgres/Milvus if configured).
Risks and side effects
  • Sandboxed code execution may have security risks; ensure inputs are trusted
  • External API calls (embedding, QA) may incur costs
  • Local database and data need proper backups to avoid loss
  • The server is not officially maintained, so compatibility or stability issues may arise
  • Search endpoints may occasionally return empty results or errors, especially when blocked
  • Content fetching may be subject to SSRF protection, blocking private or loopback addresses by default
  • Misuse could lead to rate limiting or IP blocking
  • Tools have write operations that may unintentionally modify or delete notes.
  • Edit operations may overwrite existing content, though there are protections.
  • Semantic search and reranking features may require model downloads, adding latency.
  • Local data backups are the user's responsibility.
Supported clientsClaude DesktopClaude Desktop, Claude CodeClaude Desktop, Claude Code, Codex CLI, Cursor, VS Code, ChatGPT, Obsidian
Tools4221