Enables LLMs to query live data from over 250+ sources via CData Python Connectors using natural language. Wraps connectors as a Model Context Protocol server.
Connect Claude to your Power BI semantic models. Browse workspaces, tables, and measures, run DAX queries, and get results — with large datasets automatically saved to local CSV files to protect the LLM context window. Includes a query history log for cross-session reuse and auditability.
Enables AI assistants to run reproducible bioinformatics pipelines over MCP, with verifiable provenance via checksums and Workflow Run RO-Crate metadata.
MCP server that exposes local Python scripts as tools for AI clients (e.g. Claude Desktop), enabling directory tree generation, Excel-to-text conversion, Python code extraction, and text file merging.
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.
A Model Context Protocol (MCP) server that enables MCP clients like Claude Desktop to interact with protocols.io, a popular platform for sharing scientific protocols and methods.
An MCP server that connects Claude and other MCP clients to Microsoft Power BI via its REST API using a Service Principal, enabling natural language queries to list workspaces, run DAX queries, refresh datasets, and explore reports.
Enables LLMs to interact with Google Analytics Admin and Data APIs to retrieve account summaries, property details, and custom metrics. It allows users to run core and real-time reports to analyze website performance and configuration via natural language.
Minimalistic MCP server that lets AI assistants inspect, quality-check, and clean CSV datasets through tools, resources, and prompts, without needing local file access.
MCP server for the BAIC Data Layer. Provides tools to manage sources, knowledge bases, knowledge clusters, run data agents, and create/manage pipelines.
Enables AI agents to manage Databricks Lakeflow jobs by building and uploading Python wheels and triggering runs with specific arguments. It provides a structured way to orchestrate complex data experiments and monitor execution directly on Databricks clusters.
A Python MCP server for deterministic fuzzy text matching using RapidFuzz. It enables text normalization, string comparison, best-match ranking, duplicate grouping, and match explanation via configurable profiles and strategies.