Auto Ontology MCP

What auto-ontology-mcp does

auto-ontology-mcp connects MCP clients to two related workflows: ontology-guided database analysis and a personal knowledge base. For structured data, the model can inspect a database, build business Terms, review joins, identify value representations, and ask questions using SQL that the server checks against the ontology and stored data.

The knowledge-base workflow accepts material such as receipts, statements, research papers, book notes, screenshots, and other documents. Extracted facts are stored as records, ideas become linked concepts, useful passages are indexed for full-text search, and source information is retained.

How it works

Language tasks such as reading, extraction, naming, judgment, and SQL drafting are performed by the model in the connected MCP client. The server handles deterministic operations including schema extraction, data profiling, key inference, ontology validation, storage, and read-only query execution. It stores its local working data in SQLite files and does not require an LLM endpoint or LLM API key.

For database questions, the client can check whether requested concepts are covered, retrieve relevant query context, find valid join paths, validate a draft query, and execute the corrected SQL. Validation can catch incorrect joins, mismatched stored values, percentage-scale mistakes, fan-out caused by one-to-many relationships, and questions that the available data cannot answer.

For documents, the client can preserve the original text, save passages and concepts, associate relationships and source locations, and search the resulting text index. The export_wiki capability writes the knowledge base to an Obsidian vault with aliases, wikilinks, and graph-view relationships.

Setup and configuration

The project requires Python 3.12 or newer and supports both stdio and streamable HTTP MCP transports. Connect it to an MCP client that supports one of those transports, then point the workflow at the database or knowledge-base material you want the client to process. No model provider endpoint or LLM API key is needed because language operations run through the connected client.

The auto-ontology-mcp demo command creates a fictional retail example with a synthetic database, sample knowledge-base documents, and questions combining both sources. It is useful for checking the database and document workflows before using personal data.

Tools and capabilities

• Check whether requested entities or metrics are answerable with check_answerable.
• Retrieve ontology terms, join paths, role notes, and related query guidance with get_query_context and find_join_path.
• Review generated SQL against known relationships and data values using validate_sql.
• Execute read-only SQL with run_sql.
• Save source passages and concepts through save_passages and save_concept.
• Search indexed passages with search_text and export the knowledge base with export_wiki.

Ontology relationships can distinguish roles such as billing and delivery locations, while draft Terms remain identifiable as unreviewed. Knowledge-base records preserve links back to their originating documents and passages.

Limitations and notes

The system can identify when a requested measure is not covered, but it cannot calculate a metric absent from the connected data, such as return on ad spend without advertising spend. Generated Terms may remain drafts until reviewed, so their definitions should be treated accordingly.

The server performs read-only SQL execution. Its local storage is based on SQLite, while original knowledge-base documents are kept in an Obsidian vault rather than being reduced to only extracted facts or passages.