"postgres" matching MCP servers:

A PostgreSQL MCP server for AI agents that streams large result sets to CSV, enforces per-query timeouts, auto-reconnects with backoff, and supports long-running tunnel scripts with credential rotation.

A server that connects to PostgreSQL databases and provides tools for safely exploring schemas, running read-only SQL queries, and performing data analysis with pre-built templates.

A read-only MCP server that exposes dbt project artifacts and data quality result tables (BigQuery/Postgres) to LLM clients, enabling deep introspection, run-history analysis, source freshness, test coverage, and lineage walks.

Enables AI agents to create, manage, and monitor two-way data syncs between platforms like Airtable, Webflow, HubSpot, Salesforce, Notion, and Postgres, including mapping fields, running syncs, and troubleshooting record-level issues.

Enables Claude to query a Postgres analytics schema via a read-only MCP server, with tools auto-generated from live database introspection and configurable SQL queries defined in YAML.

MCP server with 32 tools for ETL ingestion, AI-generated data quality rules, AI transformations, vector search, and natural-language SQL. Works across Postgres, MongoDB, Kafka, S3/MinIO, HashiCorp Vault, and five vector stores (Qdrant, Weaviate, Milvus, Chroma, pgvector).

Enables tracing of synthetic events through a toy Redis Streams + Postgres ingestion pipeline, answering questions about which events were dropped, duplicated, delayed, or are in flight, plus overall pipeline health. It also supports first-class fault injection and reconciles observed pipeline state against independently recorded producer ground truth to expose real loss or silent failures.

Enables MCP-compatible agents to run governed, read-only analytics queries against Postgres using predefined metrics and dimensions, with safety checks and provenance on every result.

Enables AI agents to interact with PostgreSQL databases through the Model Context Protocol, providing database schema exploration, table structure inspection, and SQL query execution capabilities.

Enables LLMs to interact deeply with PostgreSQL databases—query data, manage schema, analyze performance, and administer the database.

Syncs bank data from Pluggy to Postgres and provides MCP tools to answer natural-language questions about spending, budgets, transactions, and categorization rules.