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    An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.
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    attendance-engine-mcp is a Model Context Protocol server that gives AI agents deterministic, fixture-backed tools for workforce attendance and wage-and-hour compliance. Built on @attendance-engine/core — a pure-function, zero-deps, 100%-covered TypeScript engine — it lets Claude, Cursor, Windsurf, or any MCP host correctly answer the questions HR/payroll teams actually ask: did this person clock i
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    MIT
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    A Model Context Protocol server that enables SQL operations (SELECT, INSERT, UPDATE, DELETE) and table management through a standardized interface with SQLite databases.
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    Provides comprehensive SQLite database operations for LLMs with security features, transaction support, and separation of read-only and destructive operations.
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    Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.
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    A Model Context Protocol server that provides database interaction capabilities through SQLite, enabling users to run SQL queries, analyze business data, and automatically generate business insight memos.
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    MIT
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    A Node.js implementation of the Model Context Protocol server using SQLite, providing a npx-based alternative for environments lacking Python's UVX runner.
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    MIT
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    Enables GitHub Copilot and other LLMs to execute read-only SQL queries against Oracle databases with secure connection pooling and schema introspection capabilities.
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    AGPL 3.0
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    A Model Context Protocol server that enables LLMs to interact with databases (currently MongoDB) through natural language, supporting operations like querying, inserting, deleting documents, and running aggregation pipelines.
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    MIT
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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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    Enables AI assistants to interact with and manage multiple database types (PostgreSQL, MySQL, SQLite, SQL Server, MongoDB, Redis) through natural language, supporting query analysis, schema management, data analysis, backup/restore, and security analysis.
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    MIT