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Process arbitrarily long contexts with recursive decomposition, without external LLM APIs.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "RLM MCP Server" yet — see the docs or source repo.
Use the RLM MCP Server to recursively decompose this large codebase, summarize its architecture by module, key dependencies, main entry points, and potential refactoring opportunities, then provide an overall technical overview.
A module-by-module codebase analysis, overall architecture summary, and actionable refactoring suggestions.
Use the RLM MCP Server to process this set of lengthy papers and reports, recursively extract each document’s main arguments, methods, conclusions, and limitations, and combine them into a comparative review.
Structured summaries of multiple sources, key comparisons, and synthesized research conclusions.
Use the RLM MCP Server to read this very long document, recursively break it down by section, answer my questions about key clauses, major risks, and execution points, and produce a concise summary.
Accurate answers grounded in the full document, plus a hierarchical summary and key-point checklist.
Analyze large codebases hierarchically and build a queryable knowledge map.
Search and read indexed local documents with full-text and fuzzy matching.
Gives AI coding agents persistent memory and semantic file discovery across sessions.
Index documents and retrieve relevant context for better LLM responses.
Route LLM requests across providers and orchestrate MCP tools with local privacy.
Delegate low-risk tasks to a cheaper model with main-agent review.