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    MCP server for Durable energy trading analytics that connects Claude Desktop to Snowflake data, enabling natural-language queries about spreads, constraints, generators, and prices.
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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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    A Model Context Protocol server that allows interaction with the RubyGems.org API to fetch metadata about Ruby packages, search gems, and explore dependencies and ownership information.
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    Enables you to interact with your Fivetran environment through natural language, supporting read-only queries about connections and syncs, and write operations like updating sync frequency when enabled.
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    MIT
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    A Model Context Protocol server that provides real-time Bitcoin blockchain and mempool data to AI clients, allowing access to comprehensive Bitcoin network information through various data tools.
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    MIT
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    A Model Context Protocol server that enables working with Building Information Modeling (BIM) files by converting IFC files to fragment format and querying BIM data by category.
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    MIT
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    Enables large language models to retrieve up-to-date and correct information about dlt pipelines, datasets, and schemas, improving AI-assisted development.
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    Apache 2.0
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    Enables interaction with live Pokémon data through PokeAPI, providing comprehensive Pokémon information, battle calculations, moveset validation, and team analysis. Supports searching Pokémon and moves, calculating stats, checking type effectiveness, and analyzing team synergies with in-memory caching for improved performance.
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    A local MCP server enabling AI assistants to query and analyze data via DuckDB SQL engine, supporting local files, memory, S3, and MotherDuck.
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    Apache 2.0
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    An MCP server that enables AI assistants to safely interact with Apache Kafka clusters, providing tools for topic management, message operations, consumer groups, and cluster information.
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    MIT
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    Deterministic bank-statement parsing for AI agents: messy CSV/OFX exports to clean, categorized ledger rows. In-memory only, no storage, no external calls, no LLM in the loop.
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    MIT