P

AI Tool

Postgres MCP

Connect AI agents to Postgres databases via MCP protocol for schema-aware querying and data exploration

pg-mcp-server is a Model Context Protocol server that bridges AI agents and PostgreSQL databases. It exposes schema metadata (tables, columns, indexes, foreign keys) as MCP resources, and lets agents execute read-only SQL queries or transactional writes. Ideal for developers who want Claude, Cursor, or other LLM-powered tools to answer questions about a live database without manual SQL. Supports connection string configuration, SSL modes, and Row-level security awareness.

Category Developer Tools
Pricing Free / Open Source
Platforms macOS / Linux / Windows

Use cases

  • Ask an AI agent to explain the schema and relationships in an unfamiliar Postgres database
  • Generate Prisma migrations from an existing live database structure
  • Write and validate complex SQL queries through natural language
  • Debug data inconsistencies by querying specific rows referenced in a ticket
  • Explore table statistics and index effectiveness with AI assistance

Key features

  • Schema introspection: exposes tables, columns, indexes, and foreign keys as MCP resources
  • Read and write query execution with parameterized SQL support
  • Transaction support for safe multi-step data modifications
  • Row-level security (RLS) policy visibility for Postgres Pro users
  • Connection pooling awareness via libpq environment variables

Related

Related

3 Indexed items

Chroma

Developer ToolsOpen source

Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.

LiteLLM

Developer ToolsOpen source

LiteLLM is an open-source Python library and proxy stack documented at docs.litellm.ai that exposes a single `completion()` interface across providers such as OpenAI, Anthropic, Vertex AI, Bedrock, and Ollama using OpenAI-compatible request and response shapes. The project documents a Router with retry, fallback, and load-balancing across deployments, optional observability callbacks (Langfuse, MLflow, Helicone, and others listed in observability guides), and a self-hosted LiteLLM Proxy (LLM Gateway) with virtual keys, spend tracking, guardrails, and an admin UI. Recent documentation also describes an MCP Gateway that centralizes MCP tool access with per-key, per-team, and per-organization permissions.

OpenRouter

Developer ToolsFree + Paid

OpenRouter is a model gateway that exposes many third-party AI models through one OpenAI-compatible API. Teams can compare providers, set routing preferences, and switch models without rewriting core client logic for each vendor SDK. The service publishes per-model pricing and supports pay-as-you-go usage.