"pandas" matching MCP servers:

An MCP server for chDB, the in-process SQL OLAP engine powered by ClickHouse. Lets agents query Parquet, CSV, JSON, and pandas DataFrames with one tool — no separate server, no Docker.

An MCP server for reading, writing, and analyzing Excel files using Python, pandas, and openpyxl. It enables tasks such as managing multiple worksheets, performing structural data analysis, and creating new files from JSON data.

A spreadsheet and CSV analysis toolkit for AI agents that enables loading CSV files, filtering and querying data, computing statistics, creating aggregations, building pivot tables, and exporting chart-ready data using pandas.

Provides advanced mathematical calculation capabilities including symbolic algebra, numerical computing, data analysis, and image processing through SymPy, NumPy, SciPy, and pandas integration.

Generates complete PyAirbyte data pipeline code and setup instructions for moving data between 600+ Airbyte source and destination connectors or to Pandas DataFrames, using AI-powered context-aware guidance based on connector documentation.

A FastAPI-integrated MCP server that provides mathematical operations like addition and multiplication using Pandas for data manipulation. It serves as a comprehensive example for implementing different tool registration patterns and real-time communication via Server-Sent Events.

A FastMCP server for natural language data analysis that allows users to load CSV files, execute Python code with pandas and numpy, and generate matplotlib visualizations. It provides a suite of tools for data exploration, statistical summaries, and querying datasets through MCP-compatible clients.

Enables AI coding assistants to scan, review, and explain AI, agent, and MCP security issues, run AISVS verification, and access OWASP-anchored standards controls from the IDE via stdio.

Enables coding agents to read and write a local kanban board, adding and editing cards, moving them between columns, managing projects, and writing update entries so their work is visible in real time.

Exposes internal tools from agent harnesses (Claude Code, Codex, etc.) as a standard MCP server by intercepting LLM API calls.

Enables seamless interaction with Odoo instances through the Model Context Protocol, supporting CRUD operations, custom method execution, and real-time updates. It offers versatile communication via stdio and HTTP protocols, including support for streaming and Server-Sent Events.