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RLM-MCP

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    Related Servers

    • MCP-RLM

      MuhammadIndar
      A
      license
      Not graded
      quality
      C
      maintenance
      An 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.
      12
      MIT

    • rlm-mcp-server

      wgthomas
      F
      license
      Not graded
      quality
      D
      maintenance
      Provides recursive language model capabilities to AI assistants, enabling efficient exploration of large contexts through iterative Python code execution.
      1
      -

    • Aleph

      Hmbown
      A
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to analyze documents larger than their context window by loading files into RAM and querying them via search, navigation, and Python execution tools. Supports recursive reasoning to process massive datasets in chunks using sub-agents.
      139 PyPI
      217
      MIT

    • mcp-context-window

      donliggett
      F
      license
      A
      quality
      C
      maintenance
      Provides an external context buffer so a local model can maintain session memory across long tasks and search and page through large documents without loading them whole into its limited context window.
      15
      1
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    • CONTINUITY

      duke-of-beans
      F
      license
      Not graded
      quality
      C
      maintenance
      Provides session persistence, crash recovery, decision tracking, and context compression for AI assistants, enabling seamless multi-session continuity.
      -
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.5/5.0

    Scored across 12 tools

    Disambiguation5/5

    Each tool targets a distinct resource (artifact, chunk, doc, session, span) and action, with clear descriptions. No ambiguity between tools.

    Naming Consistency5/5

    All tools follow a consistent `rlm.<resource>.<action>` dot-separated pattern, improving predictability for agents.

    Tool Count5/5

    12 tools cover the necessary operations for a document processing server without being excessive or insufficient.

    Completeness4/5

    The tool set covers core lifecycle operations (session creation/close, document loading/peeking, artifact storage/retrieval, search), but lacks delete or update operations for several resources, which may be intentional but limits completeness.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources