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"Finding tasks for large language model agents" matching MCP servers:

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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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    MCP server for connecting to Microsoft Fabric and Power BI semantic models, enabling workspace browsing, dataset management, TMSL retrieval, DAX queries, model creation/editing, and includes a Best Practice Analyzer with 71 rules.
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    MIT
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    A fast, safe MCP server for YNAB providing read-only budget access by default, with 28 tools for budgets, transactions, categories, payees, accounts, scheduled transactions, and server-computed analytics.
    MIT
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    MCP server that connects AI assistants to Redash, enabling listing of assets, read-only query execution, dashboard inspection, and alert management through the Redash API.
    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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    An MCP server for controlling Apache Airflow 3 via its REST API, enabling operations like DAG management, task monitoring, and more through natural language.
    MIT
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    Enables LLM clients to analyze and interact with Power BI (.pbix) files, providing access to data models, DAX measures, Power Query code, table relationships, and data contents through natural language.
    MIT
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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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    A portfolio analysis MCP server that enables AI agents to manage investment portfolios, fetch financial data from Yahoo Finance and CoinGecko, and perform advanced analysis like weight optimization and Monte Carlo simulations. It utilizes reference-based caching to efficiently handle large datasets without bloating the LLM's context window.
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    MIT