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    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Enables AI assistants to navigate and populate complex ontologies by transforming unstructured data into structured, SHACL-validated knowledge graphs. It features semantic hybrid search and proactive schema guidance to help users build deeply linked, reasoning-ready data.
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    • A
      license
      A
      quality
      C
      maintenance
      Enables AI assistants to query, modify, and reason over OWL/TTL/RDF ontology files using natural language, without needing SPARQL or OWL syntax.
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    • A
      license
      A
      quality
      B
      maintenance
      A typed stdio/HTTP MCP server that lets agents drive governed portfolio-company data onboarding: profiling sources, inferring entities and joins, proposing canonical mappings, generating and sandbox-testing dbt artifacts, and publishing only human-certified results. It exposes agent and reviewer-only tools, tenant-scoped run/ontology/evidence resources, and guided prompts, keeping every calculation deterministic and every approval with a human.
      12
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables LLM agents to manage a local SQLite-backed social CRM through twelve intention-shaped tools: searching and inspecting contacts, merging duplicates, managing identities and relationships, recording events and commitments, and maintaining recurring dates, attributes, and derived graph review views.
      548 npm
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    • A
      license
      C
      quality
      B
      maintenance
      Reference implementation of the Operational Ontology pattern: MCP tools are generated from a typed business domain model (objects, links, actions) — one tool per query shape and per action, deliberately no raw SQL tool. Writes pass business-rule preconditions, are audited, and write back to the systems of record; refusals are machine-readable. Ships with a demo orders ontology spanning two legacy
      34
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    TDQS

    B3.4/5.0

    Scored across 32 tools

    Disambiguation4/5

    Most tools have sharply distinct roles (build vs inspect vs answer vs export), and the verbose Chinese descriptions spell out preconditions and return shapes. A few pairs could still be confused: find_instances vs object_rows (lookup by name vs enumerate all instances), and the ask_question / list_questions / fix_question / drop_filter cluster requires careful reading to pick correctly.

    Naming Consistency4/5

    Everything is uniformly snake_case and most names follow verb_noun (find_instances, list_objects, save_card, set_rules, build_ontology, export_ttl). The deviations are noun-only names like health, data_check, data_layout, object_rows, run_overview, which form a recognizable secondary pattern rather than random mixing.

    Tool Count2/5

    32 tools is heavy for one server, and there is clear bloat: the four export_* tools repeat an identical ~200-character paragraph about out_dir, manifest files and overwrite semantics, and card/question tooling is split across many narrow operations. The multi-phase workflow (ingest, model, verify, answer, export) justifies breadth, but the surface should be consolidated.

    Completeness4/5

    The surface covers the full lifecycle: preview upload, build ontology, health-check data, confirm types/relations/rules, answer questions with derived-metric approval, record customer answers, and export to CSV/Mermaid/Turtle/pipeline spec. Minor gaps: no way to delete or re-run a stored run, and instance-level data export beyond per-object forms is absent.

    Maintenance

    ActivityActive
    ResponsivenessNo recent community issues