"Using PostgreSQL to Execute Queries" matching MCP servers:

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

This read-only MCP Server allows you to connect to PostgreSQL data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

Enables AI clients to interact with PostgreSQL databases through natural language, translating requests into structured queries and returning results via a secure self-hosted server.

This MCP server lets AI agents connect to Figma to read, analyze, create, and modify designs, including elements, styles, assets, and code generation. It works with free Figma accounts and supports parallel multi-agent use across MCP-compatible clients.

Read-only MCP server that enables AI tools like Claude and Cursor to query the Avanti Fellows PostgreSQL database, providing schema inspection, data sampling, and SELECT query execution capabilities.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

Serves as a universal interface between AI Agents and AnalyticDB PostgreSQL databases, enabling metadata retrieval and SQL execution, with additional capabilities for knowledge graph and LLM memory management.

Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

Thin MCP server that enables AI agents to generate video, image, and audio content via the Deepy API using a personal API key, with safe generation flow and no business logic.

Enables querying of a locally pinned snapshot of the Education-to-Workforce Indicator Framework — 99 indicators and 11.8M observations — to browse questions, describe metrics, resolve places, and fetch or rank data across national, state, county, and district levels.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

Enables agents to discover available still-image animation models, obtain credit quotes without charge, submit one explicitly approved generation job, and poll the resulting short clip.

Enables searching government awards through the Sell to State API by proxying JSON-RPC to the live HTTP MCP endpoint, requiring an API key.

Lightweight project management for teams and AI agents.

Connect AI assistants to Manticore Search. Execute SQL queries, list tables, get schemas, and fetch documentation. Perfect for building RAG applications and search-powered AI agents.

Enables natural language interaction with rasdaman multidimensional databases by translating tool calls into WCS/WCPS queries. It allows users to list coverages, retrieve metadata, and execute complex queries on datacubes through an LLM.

Enables LLMs to inspect schemas and execute SQL queries on Peaka projects using Peaka's text2SQL capabilities.

Enables AI assistants to interact with 3DCityDB v5 through natural language, dynamically resolving object classes, properties, and codelists to answer spatial questions and execute SQL queries on CityGML data.

Enables AI agents to discover and query data federated across MySQL/MariaDB, PostgreSQL, ClickHouse, S3 and Kafka with SQL, exposing tools to list databases, schemas, tables and connections, describe columns, search objects, inspect table statistics, run EXPLAIN, and execute arbitrary statements. It runs locally over stdio against a PostgreSQL-wire endpoint and returns structured results or SQLSTATE-coded errors.