"How to run a Python file using Claude Desktop" matching MCP servers:

Enables Claude Code to send prompts to Claude Desktop using macOS automation and AppleScript. Supports conversation management and configurable response polling, though reading responses back is limited by Electron's accessibility APIs.

An instrumented MCP server for debugging lost tool calls in Claude Desktop, using ping, echo, sized_echo, and slow_echo tools to record and analyze the full protocol flow.

Enables AI agents to interact with the Execute.run bot API for managing Shell balances, transferring funds, and executing LLM requests. It provides tools for identity verification, transaction tracking, and performing compute tasks through the Execute.run platform.

An AI recipe recommendation server based on the MCP protocol, providing functions such as recipe query, classification filtering, intelligent dietary planning, and daily menu recommendation.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

Allows Claude to execute terminal commands on your computer and perform file system operations including surgical code editing with diff-based replacements.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

MCP server that gives Claude Desktop and other desktop MCP clients filesystem powers—read, write, edit, and manage files like AI coding assistants.

Enables a shared persistent knowledge vault for Claude Desktop, allowing notes to be saved, retrieved, searched, updated, and deleted across Chat and Code tabs.

Reduces Claude's context window costs by automatically summarizing inactive files to their public interfaces using AST parsing, keeping only the full contents of the currently active file.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

A powerful, security-hardened MCP server for intelligent file organization. Provides high-level atomic tools for organizing files into categories, detecting duplicates, extracting EXIF/ID3 metadata, scheduling automatic organization, and more — for use with Claude, Cursor, and Gemini CLI.

Enables comprehensive Trello integration through Claude Desktop, allowing users to search, create, update, and manage Trello boards, cards, lists, comments, and collaborate with team members through natural language.

MCP server exposing a parametric policy engine for evaluating, simulating, and managing rules, with dynamic plugin support for domain-specific calculations.

Enables AI assistants like Claude and Cursor to interact directly with Figma to create, modify, and analyze design elements in real-time. It provides a comprehensive suite of tools for document inspection, styling, component management, and automated layout via a bidirectional WebSocket connection.

MCP server that enables AI agents to read, analyze, and modify Figma designs, including comment management via Figma's REST API. Works with any Figma account and supports parallel execution.

macOS desktop automation enabling AI agents to screenshot, switch apps and tabs, click, type, and scroll. Offers two trust levels: read-only screenshot and full UI control.

Provides a local, agent-maintained product context layer over MCP, serving structured strategy, competitor, customer feedback, and feature inventory data to any AI tool such as Claude, Cursor, or ChatGPT, with local data storage, bring-your-own-LLM support, and weekly evidence-based roadmap reviews that can sync suggestions to Jira or Linear.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.