Schema | mcp-server-pgvector

mcp-server-pgvector

Describes the environment variables required to run the server.

Name	Required	Description	Default
`DATABASE_URL`	No	PostgreSQL connection string (e.g., postgresql://user:password@localhost:5432/mydb)
`PGVECTOR_DATABASE_URL`	No	Alternative PostgreSQL connection string
`MCP_PGVECTOR_READ_ONLY`	No	Set to 'true' to disable write tools (upsert_embedding, create_vector_index). Default is 'false'.	false
`MCP_PGVECTOR_COMMAND_TIMEOUT_SECONDS`	No	Per-command timeout in seconds. Set to '0' to disable. Default is '30'.	30

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Features and capabilities supported by this server

Protocol revision2025-11-25

Capability	Details
`tools`	{ "listChanged": false }
`prompts`	{ "listChanged": false }
`resources`	{ "subscribe": false, "listChanged": false }
`experimental`	{}

Functions exposed to the LLM to take actions

Name	Description
list_vector_tablesA	List every column of type `vector` in the database, with its table and dimensionality.
describe_vector_tableA	Get columns, indexes, and an approximate row count for a table.
similarity_searchB	Find the k nearest neighbors to a query embedding. `filters` is a structured allowlist (column/op/value) — not raw SQL — so metadata filtering stays injection-safe.
hybrid_searchA	Rank rows by a weighted blend of vector similarity and Postgres full-text search.
upsert_embeddingB	Insert a row or update it in place if `id_value` already exists (upsert).
create_vector_indexB	Create an HNSW or IVFFlat approximate-nearest-neighbor index on a vector column.
explain_similarity_queryB	Run EXPLAIN ANALYZE on a similarity query to confirm an ANN index is used.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources

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