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SlimContext MCP Server

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    • Concisr

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      A
      license
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      quality
      D
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      Token compression for AI contexts, reducing token consumption by compressing conversation exchanges before they enter the LLM context window.
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    • compresh-mcp by compresh

      compresh-mcpofficial

      compresh
      A
      license
      Not graded
      quality
      C
      maintenance
      Provides production-grade context compression for LLM agent conversations with Q-protective ranking, epistemic markers, and semantic store, reducing token usage while preserving equivalence.
      3
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    • tokenslim-mcp

      robertruben98
      A
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      quality
      D
      maintenance
      Provides context compression via the tokenslim engine, enabling MCP hosts to reduce token usage while preserving key information. Offers compress, retrieve, and stats tools for managing compressed content.
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    • cap-shield-mcp

      robinlidberg-dot
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to compress and selectively retrieve context, with measured recall rather than claimed performance. It provides tools to assess potential traffic and token savings, list compression dictionaries, and assemble relevant memory entries within a token budget.
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    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

    A3.9/5.0

    Scored across 2 tools

    Disambiguation5/5

    The two tools have clearly distinct purposes: summarize_messages uses AI-powered summarization to compress history by creating concise summaries, while trim_messages uses token-based trimming to remove oldest messages when exceeding thresholds. There is no overlap in their approaches, making them easily distinguishable.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with underscore separation: summarize_messages and trim_messages. The naming is predictable and readable, with no deviations in style or convention.

    Tool Count3/5

    With only 2 tools, the server feels thin for a context management domain, as it lacks operations like retrieval, update, or deletion of summaries/trims. However, the tools cover compression strategies adequately for a minimal scope.

    Completeness2/5

    The server is severely incomplete for context management; it only offers compression methods (summarization and trimming) but lacks any tools to retrieve, modify, or manage the compressed contexts, leaving agents with no way to access or update the results of these operations.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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