"The official Python standard library documentation" matching MCP servers:

A Python MCP server for Metabase that enables AI assistants to query databases, manage dashboards, and interact with your Metabase instance.

Enables LLM clients to run Python code in a persistent, user-selected interpreter via MCP, with tools for data loading, summaries, regressions, diagnostics, and plotting, while keeping sessions alive between calls and isolating crashes.

It enables AI assistants to execute SQL and explore databases, retrieve schemas, metrics, and overviews, and generate Plotly charts directly from query results.

Enables AI assistants to execute SQL, explore database schemas and sample data, inspect system metrics, and generate Plotly charts from query results.

It bridges AI assistants and StarRocks databases, enabling direct SQL execution, database/schema exploration, system metric access, detailed table/database overviews, and Plotly chart generation without complex client-side setup.

Enables secure execution of statistical Python code in isolated micro-VM sandboxes, generating charts and analytical summaries.

Enables time series analysis following Box-Jenkins-Treadway methodology, supporting guided or autonomous modes for model identification, estimation, and diagnosis via an LLM.

Validates and renders declarative JSON diagram specs to themeable SVG. The format has no coordinates, so a model writes structure and the renderer does the layout; tools: validate, render, kinds.

Enables rendering, validation, formatting, and inspection of D2 diagrams via the Model Context Protocol, using WASM for rendering without requiring the d2 binary.

Zero-dependency charts for Python and AI agents. SVG and PNG, 15 chart types, a one-line MCP server, and a Claude skill.

A demo MCP server that returns an interactive dashboard (bar chart with dataset selector) as an MCP App via the official MCP Apps extension (SEP-1865).

Enables users to create, inspect, analyze, format, and chart Excel workbooks by having the client LLM write Python that runs in a fresh, network-restricted Docker container. Exposes a single execute_python tool with access to openpyxl, pandas, numpy, and matplotlib, persisting files between calls.

Official draw.io MCP server: create diagrams from Mermaid, XML or CSV, shown inline in Claude, ChatGPT, VS Code and Cursor or opened in the draw.io editor. Searches 10,000+ shapes.

Data-visualization MCP server for ROOT Workspace. It enables creating and iterating on charts (Vega-Lite, Python plots) with a persistent kernel and vision loop.

A comprehensive MCP server that provides intelligent access to the HoloViz ecosystem, enabling AI assistants to help you build interactive dashboards and data visualizations with Panel, hvPlot, Lumen, Datashader, and your favorite Python libraries.

diagramzu-mcp lets an AI client (Claude, Cursor, ChatGPT, etc.) author Mermaid diagrams in a shared workspace that a team can read, comment on, embed, and present at a clean URL — rather than just generating a one-off diagram. Hosted endpoint https://mcp.diagramzu.ai/mcp or local npx -y @diagramzu/mcp. Also listed in the official MCP registry as ai.diagramzu/mcp.

An MCP server for the Xe Currency Data API that brings live FX rates, historical analysis, and quant-flavored tools into AI tools, working out of the box with zero credentials via Frankfurter/ECB data.

Visualizes conversations as structured hierarchical mind maps by injecting a prompt that instructs the LLM to generate a JSON mind map — no external APIs required.

Enables natural-language (Chinese or English) querying of a socioeconomic survey microdata warehouse covering five survey programs and over a thousand tables, by exposing standard tools for exploring surveys and sections, semantically searching variable metadata, generating read-only SQL, computing weighted statistics, and rendering charts. Results, SQL, and tool calls are returned in-chat, and it can be attached to Claude Desktop or Claude Code as well as its bundled web interface.

Lets agents and chat clients load battery cells, collect them into frames, render figures, export data, look up cellpy API calls, and set up batch projects without writing Python.