Database MCP servers for Postgres, MySQL, SQLite, MongoDB and more: let an AI assistant inspect schemas and run queries with the access you allow. 1,096 servers in this category.
Turns collections of files into a navigable knowledge graph to provide agents with structured, source-linked context packs.
Allows AI agents to inspect PostgreSQL query plans and verify performance improvements through measured, rolled-back transactions.
Lets an AI client diagnose Python backend stacks by analyzing Postgres, Celery, Redis, and logs in a single tool call.
Provides a production-grade Redis interface for LLMs with built-in security safeguards, command gateways, and automated data truncation.
Lets an AI client query a self-hosted nutrition database containing merged data from USDA, Open Food Facts, and multiple national food tables.
Provides a unified SQLite-based memory system for AI agents featuring lossless context management, graph-based recall, and semantic search.
Enables semantic search and structured metadata queries over personal documents, emails, and records using pgvector and OpenAI-compatible embeddings.
Performs read-only PostgreSQL performance auditing to identify slow queries, index issues, and configuration bottlenecks.
Enables AI agents to perform database health checks, query plan analysis, and index tuning on PostgreSQL databases via an authenticated gateway.
An on-premise knowledge layer that makes existing databases and SaaS applications queryable by AI agents via the Model Context Protocol.
Creates read-only SQL data agents by indexing SQLAlchemy-compatible databases into LanceDB for LLM exploration via MCP and REST.
Enables AI clients to query and retrieve microbial nomenclature, genome data, and publication information from the SeqCode Registry database.
Provides an AI agent with a database schema grounded in actual data and described in natural language sentences to improve text-to-SQL accuracy.
A knowledge-graph engine that transforms heterogeneous sources like forums and databases into cited, confidence-bounded, and trust-tiered answers.
Enables AI assistants to analyze semantic models, trace data lineage from SQL sources to Power BI visuals, and edit TMDL files in PBIP projects.
Allows an AI client to query GitHub activity using plain-English questions by generating and executing SQL against a DuckDB database.
Provides a secure Policy Enforcement Point between AI agents and PostgreSQL databases using OAuth 2.1, RBAC, and real-time semantic guardrails.
Provides a semantic vector search engine and catalog for AI agent skills, MCP servers, project directives, and system rules.
Enables AI agents to perform hybrid search and recall across local codebases, documentation, and session notes using a single SQLite file.
Provides a zero-dependency local memory layer for AI coding agents using SQLite FTS5 for persistent, offline context retrieval.
