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market-research

by autkucakan

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    • A
      license
      Not graded
      quality
      A
      maintenance
      Makes AI research agents accountable by giving every conclusion a traceable argument graph. Provides a persistent argument graph where claims require grounds and warrants for auditable, verifiable reasoning.
      2
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI agents to create verifiable, replayable citations, search private knowledge bases, and publish Markdown with verified citation markers.
      MIT
    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to perform deep research across multiple sources, arbitrating consensus while evaluating source authority and detecting contradictions. It integrates via MCP with clients like Claude Desktop and Cursor to deliver deterministic, structured research dossiers.
      7
      -
    • A
      license
      A
      quality
      C
      maintenance
      Enables LLM agents to acquire token-budgeted, deterministic context packs from repositories, with hash-chained provenance for auditability.
      2
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Provides local, evidence-aware research memory with human review, revision history, and an inspector, allowing assistants to preserve source reports, inferences, assumptions, and evidence across sessions.
      4 npm
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Enables AI agents to verify technical claims against supplied evidence, identify unsupported assumptions and contradictions, and recommend the smallest next check before acting.
      5
      MIT

    TDQS

    B3.2/5.0

    Scored across 47 tools

    Disambiguation4/5

    Most tools target distinct stages of the research pipeline, and descriptions clarify boundaries (e.g., lexical vs. semantic search, single vs. batch operations). However, the presence of legacy and batch variants (e.g., verify_claims vs. resolve_claims_batch) and multiple extraction submission paths introduces some ambiguity that an agent could misselect.

    Naming Consistency5/5

    All tool names use snake_case with a clear verb_noun pattern (e.g., create_research, get_research_state, submit_extracted_signals). Batch variants consistently add a _batch suffix, and no camelCase or mixed conventions appear.

    Tool Count2/5

    47 tools is far beyond the typical 3-15 range and exceeds the 25+ threshold for 'too many'. While the domain is complex, the sheer number increases cognitive load and risks tool selection errors.

    Completeness5/5

    The surface covers the full research lifecycle: creation, state management, stepping, extraction, verification, counterevidence, clustering, query lattice, source operations, search, comparison, analysis, and export. No obvious gaps in core workflows are apparent.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues