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"Database Query and Information Retrieval" matching MCP servers:

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    Connect AI agents to SQL databases (SQLite, PostgreSQL, MySQL) with a unified interface for querying data, exploring schemas, inserting rows, and exporting results to CSV. Includes safety features like dangerous query blocking and write guards.
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    A Model Context Protocol server for querying PyPI package information, dependencies, and compatibility checking. Supports advanced dependency analysis, download statistics, and trending analysis.
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    Enables IDE access to Supabase databases with SQL query execution, schema management, Auth admin operations, and built-in safety controls to prevent accidental destructive actions.
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    A fault-tolerant, stealth-enabled Model Context Protocol (MCP) server for web searching and content fetching. Built for AI Agents (Cursor, Claude Code, OpenCode), it uses a stealth browser engine to fetch pages, dynamically handles SPAs/React, and converts bloat into token-optimized Markdown.
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    ✨ 为你的 AI 助手装上 B 站的眼睛:一键提取视频字幕与热门评论,助力高效信息总结 🚀 ✨ Equip your AI assistant with "Bilibili Eyes": One-click extraction of video subtitles and popular comments for efficient information summarization 🚀
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    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.
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    Connects AI agents to the OpenAlex scholarly database, enabling search and retrieval of works, authors, institutions, and sources via natural language.
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    A Model Context Protocol server providing LLMs with access to the Ensembl genomics database, enabling AI assistants to query gene information, sequences, variants, and other genomic data across multiple species.
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    Embedded memory and retrieval engine for AI agents, providing local-first memory with MCP support for multi-agent access control.
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    Zero-config SQL profiler for slow Python/SQLAlchemy jobs, built for agents: returns the exact call site, query count, and fix as compact machine-readable fields instead of raw query logs~55× fewer tokens per diagnosis (≈10k → ≈180) and the matching drop in API cost.
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    MIT