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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
  • A
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    Agent-ready financial intelligence tools for AI agents. Two curated tools — get_stock_snapshot and get_company_metrics — that combine multiple data sources, derive signals (UNDERVALUED, STRONG, ACCELERATING), and pre-compute the math. One call, one agent-friendly response.
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    MIT
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    The geospatial toolkit for AI agents. 6 tools, zero config — give any LLM the ability to parse, convert, query, and aggregate spatial data out of the box. Or just make a bounding box :) Every response includes a verification link to vibhorsingh.com/boundingbox so you can visually confirm results on an interactive map.
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    MIT
  • F
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    A tutorial implementation MCP server that enables analysis of CSV and Parquet files. Allows users to summarize data and query file information through natural language interactions.
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    Enables AI assistants to interact with Databar.ai's data enrichment API, allowing discovery, configuration, and execution of data enrichments across hundreds of providers using natural language.
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    MIT
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    Enables users to analyze local Excel and CSV files through natural language queries and a web dashboard while keeping data local. It supports saving specific analyses as reusable tools and building a custom analytics toolkit within Claude Desktop.
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    10
    MIT
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    An MCP server that provides data visualization and machine learning tools, featuring automated intent-based pipeline routing for data cleaning and model training. It enables LLMs to process CSV or JSON data to generate visual charts, perform regressions, or execute clustering analysis.
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    Enables LLM agents to interact with the MOSTLY AI Platform for generating synthetic data, including training generators, managing data connectors, executing queries, and generating synthetic datasets with OAuth 2.1 authentication.
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    2
    Apache 2.0
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    Exposes ERP data (production orders, inventory, BOM, customers) to any MCP client through a backend-neutral interface, with a mock backend for testing.
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    MIT
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    Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.
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    MIT