"Information about PG Vector" matching MCP servers:

Archived MCP server for PostgreSQL health monitoring; functionality merged into pg-dash.

PG-MCP is an HTTP server implementation that enables AI systems to interact with PostgreSQL databases via MCP, providing tools for querying, connecting to multiple databases, and exploring schema resources. The system enriches context by extracting table/column description from database catalogs.

An MCP server that provides targeted PostgreSQL schema access to AI agents, retrieving only relevant tables for a question and enforcing read-only access through multiple layers.

A secure PostgreSQL Model Context Protocol server that provides read-only database access through tools like schema listing, object inspection, query execution, and EXPLAIN analysis, with database-enforced access control via read-only transactions and a minimal role rather than SQL text parsing.

Diagnoses and manages PostgreSQL logical replication including slot WAL retention, walsender lag, stuck subscriptions, and provides gated remediation tools for safe AI-driven operations.

Enables read-only PostgreSQL interaction through safe query execution, query planning, table and column inspection, and privilege checks, with multi-layer defenses against write operations and transaction-control exploits.

Enables LLM assistants to analyze PostgreSQL query execution plans with EXPLAIN and receive structured reports highlighting performance bottlenecks, all through read-only database access.

Enables read-only exploration of a Postgres database using natural language, with multiple safety layers to prevent any modifications.

Enables AI agents to run read-only Postgres bloat diagnostics, returning per-table and per-index verdicts with supporting evidence, actionable fixes, and timeline history. It also identifies blocking sessions and can take a one-shot throwaway snapshot for live production diagnosis without touching stored timeline data.

A local stdio MCP bridge that enables Cursor to interact with a remote PostgreSQL database through a PHP/Apache HTTP tunnel.

Diagnoses Postgres job queues (pg-boss, graphile-worker) to identify retry storms, stuck jobs, missed schedules, and other issues, providing evidence-based recovery steps.

MCP server providing deep PostgreSQL context to AI assistants, including schema DDL, index health, foreign key associations, query execution plans, and performance statistics via read-only tools.

Gives agents read-only access to PostgreSQL databases, enabling them to inspect schemas, sample rows, compare stands, view query plans, and run read-only SQL through a single entry point.

Enables AI assistants to discover allowed PostgreSQL schemas, run parameterized SELECT queries, and inspect query plans safely through parser-checked read-only transactions, timeouts, row caps, and role-based access controls.

MCP server for PostgreSQL with typed tools (list, describe, read, aggregate, insert, update, delete), catalog SQL served as named tools, and free SQL that runs under the grants of each login. Each caller is limited to its own tools, databases and tables, and start checks refuse unsafe setups, such as a superuser login with free SQL on. Serves stdio and Streamable HTTP. Written in Go, Apache-2.0

Enables users to interact with PostgreSQL databases using natural language, converting questions into validated, safe SQL queries and returning execution results with confidence scoring.

Enables read-only PostgreSQL schema exploration through natural language, including tables, indexes, sizes, sample rows, and query plans, with structural safeguards against writes.

An MCP Server implementation that integrates the Balldontlie API, to provide information about players, teams and games for the NBA, NFL and MLB.

Enables AI assistants to query PostgreSQL databases, inspect schemas, and retrieve complete DDL with built-in read-only protection. It supports multiple database connections and allows for secure database interaction and exploration via the Model Context Protocol.

A local, fully-offline MCP memory server that enables persistent storage and retrieval of information using SQLite with both keyword and semantic vector search capabilities.

Enables MCP-capable agents to operate PostgreSQL and PgBouncer clusters on Kubernetes—covering discovery, pooling, tuning, backups/PITR, DR, and lifecycle—with safe-by-default read-only mode and granular opt-in security controls.

Enables an AI assistant to access many named PostgreSQL database connections at once, selecting connections by name or alias and reusing pools for queries, schema inspection, and analysis tools. It supports read-only or unrestricted access modes, live config reloads, and safe result truncation.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Enables MCP clients to explore PostgreSQL schemas, run guarded read-only queries with row limits, and inspect execution plans without executing them. It prevents writes and DDL while exposing schema discovery tools, a schema overview resource, and a guided analysis prompt.

Enables Claude to answer questions from PostgreSQL databases using read-only SQL queries, with row caps, column masking, and an audit log while preventing data changes.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

A lightweight Postgres MCP server for safe database exploration and query analysis, read-only by default, with multi-database support.

High-privilege PostgreSQL admin MCP server for database and role/grant management.

🧠 A high-performance PostgreSQL-backed MCP server acting as a super memory, task tracker, and dynamic database manager for AI agents. Features built-in connection pooling, a professional tasks schema, and a unique shared multi-agent coordination hub to prevent coding conflicts in real-time.

Enables large language models to retrieve up-to-date and correct information about dlt pipelines, datasets, and schemas, improving AI-assisted development.