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universal-research-mcp

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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

    C2.8/5.0

    Scored across 30 tools

    Disambiguation2/5

    The high-level domains are clear, but the governance cluster is overcrowded with near-synonymous non-executing operations such as evaluate_gate, evaluate_operation, validate_decision, capture_codex_decision, prepare_codex_dispatch, and codex_prepare_agent_control. An agent could easily select the wrong governance tool because the boundaries between 'evaluate', 'validate', 'capture', and 'prepare' are subtle and heavily overlapping.

    Naming Consistency4/5

    Most tools follow a consistent domain_verb_noun snake_case pattern, e.g., governance_validate_task_packet, research_prepare_ingest, memory_fetch_evidence, and codex_host_agent_status. Minor deviations like memory_latest, research_semantic_models, and public_demo_status break the verb-first pattern, but the overall style remains predictable.

    Tool Count2/5

    Thirty tools is too many for a research MCP, especially when fifteen of them are governance variants that mostly 'prepare', 'validate', or 'evaluate' without executing. The tool surface feels bloated and micro-specialized rather than well-scoped, and several governance tools could likely be consolidated.

    Completeness3/5

    The core immutable ingest and evidence lifecycle is covered: prepare_ingest, commit_ingest, pending status, memory search, evidence fetching, eligibility checks, and ledger audit. However, there are notable gaps such as no way to list or cancel pending drafts, no general research query beyond memory candidates, and no direct approval path, though the host-owned design explains some of these omissions.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources