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Vedant9500

OpenLMlib

by Vedant9500
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    • Librarian

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      Provides AI agents with persistent knowledge storage, enabling them to store, search, and retrieve text, documents, and files using semantic and keyword search via MCP tools.
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    • memento

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    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

    B3.1/5.0

    Scored across 76 tools

    Disambiguation1/5

    Multiple tools have nearly identical purposes: search_knowledge, search_findings, retrieve_findings, retrieve_context, search_memory, and query_memory all retrieve knowledge with only subtle differences. Session and message reading also overlap heavily (session_context vs get_session_state, read_messages vs poll_messages vs tail_messages), so agents will frequently select the wrong tool.

    Naming Consistency2/5

    Subsets are internally consistent (save_finding/list_findings, create_session/join_session/leave_session), but the overall set mixes styles: session_start vs start_research, session_end vs end_session, search_findings vs search_knowledge, and help_library vs help_collab. Several tools are single nouns or noun phrases, and get_observations is explicitly deprecated but still exposed.

    Tool Count1/5

    76 tools is far beyond a well-scoped MCP server; even 25+ is considered heavy. The set spans findings, session memory, collaboration, OpenRouter model lookup, templates, analytics, and Co-Scientist workflows, so most tools are tangential to any single task.

    Completeness2/5

    Despite the large surface, there are obvious lifecycle gaps: delete_finding says to 'update instead' but no update_finding tool exists, and save_finding encourages updating duplicates without providing an update path. Artifacts and templates similarly lack update/delete operations, leaving agent workflows with dead ends.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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