"Plotly Dash - A Python Framework for Building Interactive Web Dashboards" matching MCP servers:

Renders 45+ interactive chart types, dashboards, and KPI widgets directly inside AI conversations. Supports drill-down, live API polling, 20 themes, and one-click export to PNG, PowerPoint, and A4 documents.

A MCP server that collects interactive user feedback during AI-assisted development, reducing unnecessary tool calls and costs by enabling AI to confirm with users.

Provides tools to interact with the Dash documentation browser API, allowing users to list installed docsets, search across documentation, and enable full-text search.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

Provides 12 tools to query South Korean building register data, including title sheets, floor details, and official house prices via the data.go.kr API. It enables users to perform smart building lookups and region code searches using natural language.

Enables interactive LeetCode practice with AI-guided authentication, problem solving, solution submission, and learning mode.

Enables AI to present options and launch an interactive web or terminal interface for user selection, then return the results to the AI.

Provides a production-ready FastMCP Python server boilerplate with structured logging, rate limiting, and config validation for building MCP servers compatible with Claude and other agents.

Enables AI agents to render interactive user interfaces such as forms, dashboards, charts, tables, and wizards directly in MCP-compatible clients. Supports structured data collection and richer interactions beyond text responses.

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

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

A MCP server that enables human-in-the-loop workflow in AI-assisted development tools by allowing users to run commands, view their output, and provide textual feedback directly to the AI assistant.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

MCP server for managing interactive processes, enabling AI agents to start, interact with, and terminate long-running programs like SSH sessions, REPLs, and installers via read/write operations.

Unified MCP server integrating NIST and OWASP security frameworks with live vulnerability data, enabling security searches, compliance mapping, threat modeling, and checklist generation.

Provides a local MCP server for AI-powered web crawling on Windows, enabling agents to directly crawl and extract web content without requiring WSL.

This MCP server provides direct access to ruff linting, formatting checks, and ty type-checking for Python projects, with token-efficient, structured output.

MCP server that connects any AI agent to MATLAB — execute code, async jobs, interactive Plotly plots, custom tools, and monitoring dashboard

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.