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    50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.
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    MIT
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    A FastMCP integration server that provides access to Apache Gravitino metadata management APIs, allowing users to manage catalog/schema/table metadata, tags, and user-role information through a structured interface.
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    Apache 2.0
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    Enables AI to interact with Microsoft Access (.mdb, .accdb) and SQLite 3 databases, including read-only support for Access 97 databases. Supports executing SQL queries, importing/export data from CSV and Excel files, and storing notes about database files.
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    MIT
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    MCP server for configuring the konsol Frappe app via the CLI. Enables AI clients to manage dimensions, measures, fact tables, connectors, and schemas using natural language.
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    A bridge that allows AI agents to access and manipulate Odoo ERP data through a standardized Model Context Protocol interface, supporting partner information, accounting data, financial records reconciliation, and invoice queries.
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    MIT
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    Provides stock market data and computes technical indicators like EMA, MACD, and RSI across multiple timeframes using the LongPort API and TA-Lib. It enables users to perform detailed financial analysis and multi-timeframe market reviews through standard MCP tools.
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    Enables schema-aware exploration of JSON data by uploading samples, flattening nested structures, and using heuristic search with token overlap and fuzzy matching to find field paths for target names, accelerating ETL and API onboarding workflows.
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    AgentShare delivers structured product search and pricing signals for AI agents over REST and MCP (Streamable HTTP). Responses include freshness & coverage metadata so agents can reason about data recency. API keys secure billed endpoints; public discovery at /agent.json and /mcp.json. Currently integrates connected marketplaces and affiliate feeds – roadmap expands to global e-commerce (AliExpre
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    MIT
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    Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.
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    6
    MIT