"Startups in AI, Large Language Models, and AI Agents" matching MCP servers:

Enables AI-powered interaction with Metabase instances and PostgreSQL databases through natural language. Creates models, SQL queries, metrics, and dashboards using both Metabase API and direct database connections.

Enables chat, document search (RAG), and a declarative SQL tool builder over MCP so any client can use these capabilities.

Enables AI agents to interactively interrogate database schema metadata using SchemaCrawler.

Enables governed interaction with synthetic people data through MCP tools, providing per-caller scoped access, semantic-layer metrics, and read-only SQL with authorization checks.

Enables natural language data queries with statistical rigor and a semantic layer, supporting 23+ analytical tools for product analytics.

Enables AI agents to interact with heterogeneous databases (MySQL, MongoDB, BigQuery) through MCP tools, allowing natural language queries and database operations.

Universal database copilot for diagnostics, operations, and performance analysis via MCP and CLI.

MCP server enabling text-to-SQL on PostgreSQL databases using a CrewAI multi-agent system. It generates and executes read-only SQL queries from natural language, with security layers against prompt injection.

Enables AI agents to conversationally interact with genomics research networks for data analysis and discovery across multiple Omics AI Explorer platforms. It provides tools for exploring data collections, examining table schemas, and executing SQL queries against datasets like Viral AI and Neuroscience AI.

Provides FastMCP-based servers with various tools for AI assistants including math operations, file management, note-taking, todo management, and database operations. Offers both basic and advanced server configurations with tools, resources, and prompt templates.

Enables AI assistants to use Neo4j knowledge graphs and Qdrant vector databases for hybrid reasoning, combining structured facts with semantic search for advanced knowledge management, research analysis, and standardized coding workflows.

A Model Context Protocol (MCP) server for developing Django applications. It exposes Django project information through MCP tools, enabling AI assistants to better understand and interact with Django codebases.

A production-style MCP server exposing six business tools (web search, database, CRM, email, calendar, analytics) to AI clients via a secure, authenticated interface, with mock mode and a reference agent client.

Enables a unified AI assistant that combines document retrieval (RAG), database queries via MCP tools, and web search, allowing users to ask complex questions and receive answers from multiple enterprise sources.

Sql Builder AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables querying a PostgreSQL database using natural language by retrieving context from Azure AI Search and generating SQL with Azure OpenAI, with validation and optional execution.

MCP server for Metabase that gives AI agents full access to query, explore, and manage content in your Metabase instance.

Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

Enables AI agents to inspect, query, test, and safely fix dbt projects and BigQuery warehouses under policy-enforced controls, with read-only direct queries and dbt-only allowlisted writes.

Exposes a single HTTP endpoint that aggregates tool definitions from GitHub, Kubernetes, and database namespaces, letting clients list available tools and invoke them by name. It provides health-check, tool-discovery, and tool-call routes for local, read-only experimentation without calling hosted models or applying production changes.