"An introduction to SQL and database queries" matching MCP servers:

Enables LLM agents to access Global Database's company intelligence, including company profiles, financials, ownership, people search, prospecting, and KYB/compliance lookups against official registries.

Provides SQLite database analytics operations through MCP, enabling AI assistants to connect, query, explore schema, and export data from SQLite databases.

Loads CSV files into a temporary SQLite database and provides comprehensive data analysis tools via MCP, enabling AI assistants to query, analyze, and export data using natural language.

Provides read-only access to SQL Server databases via Data API Builder, with tools to describe entities, read records, and aggregate data through the Model Context Protocol.

An employee-analytics MCP server backed by SQLite that provides hand-written SQL queries for common analytics tasks like salary ranking and department statistics. It self-seeds sample data, enabling immediate testing of GROUP BY, window functions, NULL handling, and subqueries.

Enables access to comprehensive vehicle information including VIN decoding, license plate OCR, vehicle history checks (theft, title, salvage records), market valuations, specifications, and warranty data for vehicles across North America and Europe.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Converts PDF files to text for use with MCP-compatible applications like Cursor IDE.

Enables AI assistants like Claude to query, analyze, and summarize local SQLite e-commerce databases using natural language through 11 tools, schema resources, and pre-built analytical prompts.

SaaS Database MCP by Gralio.ai

Give AI agents cited funding, money-market, FX and capital-market evidence through 16 public MCP tools. Keep source dates, freshness checks and coverage gaps attached; China macro coverage is metadata-only. No API key is required for the public surface. Research context only; no trade execution.

MCP server for the STRING protein–protein interaction database, hosted by STRING at mcp.string-db.org. Adapted for LLM grounding: responses are size-limited and include metadata and notes on interpretation and next steps. Tools cover identifier resolution, interaction networks and evidence, functional and PPI enrichment, and sequence similarity search.

Enables offline local LLM analysis of text data via MCP, taking raw text and an instruction to return analysis results without cloud calls.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Deterministic US equity-compensation tax optimizer. Six tools cover ISO/AMT exercise scheduling, NSO sell-vs-hold, RSU vest-and-sell, single-stock concentration, protective put / zero-cost collar pricing, and Section 1202 QSBS qualification. Federal plus 50-state plus DC tax math.

One-call Australian health and welfare data plumbing via AIHW — cited responses for practical analysis, not a data broker.

Given a company domain or name, it checks ten app marketplaces — HubSpot, Salesforce AppExchange, Slack, Zapier, Shopify, Atlassian, Chrome Web Store, Microsoft, Google Workspace, and WordPress — to report where the company is listed, with each listing's URL, installs, rating, review count, and match confidence. Useful for competitive research and partner-ecosystem audits across many domains in a single run.

An MCP-native pipeline for collecting structured intelligence on higher education institutions using the WHED schema, enabling scraping, extraction, validation, and saving of profiles.

This project implements a Model Context Protocol (MCP) server providing Formula One racing data using the Python FastF1 library. Inspired by an existing TypeScript server, it offers similar F1 data functionalities natively in Python via FastF1.

Enables researchers to turn property-history questions into reviewable evidence dossiers by gathering sources, tracing people and places, linking conclusions to supporting or opposing passages, and exporting portable backups.