RLM MCP Server
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- AlicenseNot gradedqualityDmaintenanceAn implementation of the Recursive Language Models architecture that enables AI agents to process massive documents by programmatically decomposing them into sub-queries. It allows for cost-effective and accurate reasoning across millions of tokens by treating long-form data as an external environment for root and worker models.11MIT
- FlicenseNot gradedqualityDmaintenanceProvides recursive language model capabilities to AI assistants, enabling efficient exploration of large contexts through iterative Python code execution.1-
- AlicenseAqualityBmaintenanceEnables LLMs to chat with long PDFs using a reasoning-based, tree-structured document index that navigates content like a human would, without requiring vector databases or hitting context limits.10332 npm411MIT
- AlicenseNot gradedqualityAmaintenanceEnables token-efficient analysis of documents far larger than an LLM context window by executing constrained symbolic commands against server-side handles instead of chunking or vector databases. It supports recursive queries, multi-document contexts, and memoized context stubs for compact agent interactions.4 npmMIT
- AlicenseAqualityDmaintenanceImplements the Recursive Language Model pattern, enabling LLMs to process arbitrarily long contexts through session-based document management, on-demand chunking, BM25 search, and artifact storage.121MIT
- AlicenseNot gradedqualityAmaintenanceEnables an LLM to analyze documents far larger than its context window by emitting constrained symbolic S-expression commands that are validated and executed server-side, avoiding vector databases and chunking heuristics. Large intermediate results are kept as in-memory handles so the model carries compact stubs instead of full text, and recursive sub-queries let it reason over structured portions of a document.4 npmMIT
TDQS
Scored across 17 tools
Each tool has a clearly distinct purpose: loading context, retrieving metadata, reading portions, decomposing, fetching chunks, searching, executing code, managing variables, managing answers, and session management. No two tools are interchangeable, and overlapping tools like rlm_search_context and rlm_find_all are differentiated by regex vs. substring search.
All tool names follow a consistent rlm_<verb>_<noun> pattern with snake_case (e.g., rlm_load_context, rlm_get_context_info, rlm_decompose_context). Verbs and nouns are clear and predictable, making the API easy to navigate.
With 17 tools, the set is slightly above the ideal 3-15 range, but the count is justified by the comprehensive workflow: context loading, analysis, decomposition, search, code execution, variable storage, answer management, and session lifecycle management. Each tool adds meaningful functionality.
The tool surface covers the full RLM processing lifecycle: load context, inspect structure, read specific sections, decompose into chunks, retrieve chunks, search, manipulate via code, store intermediate variables, build answers incrementally, and manage sessions. There are no obvious gaps or dead ends for the stated purpose.