LDR's MCP server is the project's own official integration, exposing its multi-engine search, private-document analysis, and cited report generation through 8 tools over local STDIO transport. It's compelling for privacy-focused users who already run local or cloud LLMs and search infrastructure, but the docs are explicit that the server lacks authentication and rate limiting and is meant for local, single-machine use rather than network deployment.
Strongest · Maintenance 18/20 Weakest · Reliability 9/20
Static review · not runListed 2026-08-06
Read the FMRS scoring method →Runtime：Python · Docker
LDR_LLM_PROVIDER
Selects the LLM provider, e.g. openai or ollama, set in the MCP client's env config.
LDR_LLM_OPENAI_API_KEY
OpenAI API key required when using the openai provider; obtain it at platform.openai.com.
LDR_LLM_OLLAMA_URL
Ollama server URL (default http://localhost:11434), set when using a local Ollama instance.
1) Install the package with MCP extras: pip install "local-deep-research[mcp]"; 2) set up an LLM source (local Ollama/LM Studio/llama.cpp, or a cloud provider API key for OpenAI/Anthropic/Google/etc.); 3) add an mcpServers entry to claude_desktop_config.json (Claude Desktop) or .mcp.json / ~/.claude/mcp.json (Claude Code) with command set to ldr-mcp and any needed LDR_* variables passed via env (e.g. LDR_LLM_PROVIDER, LDR_LLM_OLLAMA_URL, LDR_LLM_OPENAI_API_KEY); 4) restart the Claude client to pick up the config; for web search, also run SearXNG or another configured search engine.
{
"mcpServers": {
"local-deep-research": {
"command": "ldr-mcp",
"env": {
"LDR_LLM_PROVIDER": "openai",
"LDR_LLM_OPENAI_API_KEY": "sk-..."
}
}
}
}
Shown for Claude Desktop. Other clients may use a different file or key (VS Code uses "servers") — the configurator below converts it.
{
"servers": {
"local-deep-research": {
"command": "ldr-mcp",
"env": {
"LDR_LLM_PROVIDER": "openai",
"LDR_LLM_OPENAI_API_KEY": "sk-..."
}
}
}
}
Goes in your project's .vscode/mcp.json (VS Code uses a "servers" key).
claude mcp add local-deep-research -e LDR_LLM_PROVIDER=openai -e LDR_LLM_OPENAI_API_KEY=sk-... -- ldr-mcp
Run it in a terminal; replace any <…> placeholders with your own values first.
After pip install "local-deep-research[mcp]" and configuring the client, confirm the 8 tools (search, quick_research, detailed_research, generate_report, analyze_documents, list_search_engines, list_strategies, get_configuration) appear in the client's tool list; calling list_search_engines and getting a result proves the connection works.
Once connected, you can ask your AI assistant things like:
No matching tools
Listed from the project's documentation, not tested by this site.
Local Deep Research (LDR) is an open-source AI research assistant that runs local LLMs (Ollama, LM Studio, llama.cpp) or cloud models (OpenAI, Anthropic, Google, and more), searches across 10+ engines (arXiv, PubMed, Semantic Scholar, SearXNG, GitHub, etc.) plus a user's own private documents, and synthesizes findings into cited summaries or full reports. User data lives in a per-user, AES-256-encrypted SQLCipher database, with passwords never stored. The same repository ships an MCP server so Claude Desktop and Claude Code can drive these research capabilities directly; the docs state it is designed strictly for local use via STDIO transport, with no built-in authentication or rate limiting.
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Source revision c1e570b8dc7c Data synced 2026-11-04 Read the FMRS scoring method