ontopoc
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- AlicenseNot gradedqualityNot gradedmaintenanceEnables 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.-
- AlicenseAqualityCmaintenanceEnables AI assistants to query, modify, and reason over OWL/TTL/RDF ontology files using natural language, without needing SPARQL or OWL syntax.146MIT
- AlicenseNot gradedqualityBmaintenanceExposes Iceberg-backed ontology objects, links, and actions as typed MCP tools for LLM agents, enabling governed data access and operations without raw SQL.MIT
- AlicenseAqualityBmaintenanceA 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.12MIT
- AlicenseNot gradedqualityBmaintenanceEnables 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 npm1MIT
- AlicenseCqualityBmaintenanceReference 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 legacy34210MIT
TDQS
Scored across 32 tools
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.
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.
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.
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.