gDown for AI

An AI assistant needs three things from a database: to understand its model, to find what it is looking for by meaning as well as by words, and to write without being able to break anything. gDown has all three built in, in the same engine and with the same rights as every other client.

MCP server

POST /mcp speaks the Model Context Protocol: Claude or any MCP client reads the schema (JSON, Mermaid, Cypher, JSON Schema, GraphQL), runs read queries, asks for a query plan without running it, and writes. It acts with the identity of its own token or account, so with its roles and privileges; a tool it may not use is not even listed.

Vector search

CREATE VECTOR INDEX on an embedding property, then db.index.vector.queryNodes returns the k nearest neighbours by cosine or Euclidean similarity. Exact search up to 2,000 vectors, then an approximate HNSW graph (measured recall of at least 0.9) that follows updates.

Full-text search

CREATE FULLTEXT INDEX on several labels and properties, with analyzers (standard, simple, english, keyword...) and queries by terms, +required, -excluded, "exact phrase", prefix*, fuzzy~ and property:term, scored by tf-idf.

Both searches are queries like any other, so they combine with the graph in one statement: find the passages closest in meaning to a question, then follow the relationships to their source, author or product, and give the assistant that context. That is the basis of retrieval for AI answers (RAG) on connected data.

Writing without breaking anything

  • Dry run: a write runs in a transaction that is rolled back, and the agent sees the changes it would make.
  • Direct or staged: by default the write is applied at once, within the agent's rights; in staged mode it becomes a proposal, stored in a replicated database, that a reviewer promotes or rejects.
  • Rights of its own: give the agent a role limited to the databases and labels it needs, not the built-in writer role; it then cannot reach the proposals database.
  • Limits and trace: rows returned, statement size and writes per minute are capped; writes appear in the audit log (protocol mcp) and carry $_agent, $_at and $_source.

Limits: the vector and full-text indexes live in memory and are rebuilt when the database opens; no tool for saved queries (My APIs), no server-to-client stream, no OAuth specific to MCP (token or account, like the other routes). No benchmark of vector search or of MCP is published yet: the figures on this page do not cover them.