"Techniques for Writing Strong Code in C++, Rust, and Python" matching MCP servers:

Enables MCP clients to load eight on-demand coding skills and inspect project repositories through bounded, read-only map, search, read, diff, checks, and style tools. It keeps context lean by loading skill bodies only when needed and limiting repository scan and output sizes.

An MCP service for AI-assisted reasoning and editing on long-form fiction projects. It builds a structured index from scene files for targeted context retrieval.

A Model Context Protocol (MCP) server for debugging and operating Rust Tauri applications over WebSocket.

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

Enables bounded, source-write-free Rust development workflows: inspect Cargo workspaces offline, search and read source, collect compiler diagnostics, select and run tests or binaries/examples, resolve exact-version docs, and produce verified navigation or change/refactor packages without writing the workspace.

MCP server offering text analysis tools for writing improvement, including spellcheck, readability, keyword analysis, passive voice detection, and AI-generated content detection.

A MCP server to allow the LLM in Cursor to access Rust Analyzer, Crate Docs and Cargo Commands.

Provides local, deterministic scoring and detailed auditing of text for AI-writing patterns via two read-only tools, with no network calls or language models.

Enables AI coding assistants to browse Unreal Python API documentation, search classes and functions, and execute Python code in Unreal Editor.

Bridges Claude Code CLI with Cursor IDE to use your existing Claude subscription without separate API costs.

A simple MCP server that integrates Rust functions via WebAssembly, enabling operations like factorial computation through Claude Desktop.

Enables AI tools to read and write live Figma documents through a plugin bridge rather than the REST API, avoiding tokens and rate limits. Exposes 73 tools for creating and modifying frames, text, components, styles, variables, prototypes, and pages, plus screenshots, PDF/token export, and built-in design-strategy prompts.

A Model Context Protocol server for deep codebase understanding of Python projects, focusing on data analysis and scientific computing. It provides architectural analysis, pattern detection, dependency mapping, test coverage analysis, and AI-optimized context generation.

Persistent project context in Rust. 8 MCP tools via rmcp SDK — parse, validate, score, compress, discover, and token analysis. Single binary, zero config. IANA-registered format (application/vnd.faf+yaml). One file, every AI platform.

An MCP server that enables AI tools to read and write Figma designs via a plugin bridge, bypassing the Figma REST API and rate limits.

Standardizes front-end JavaScript reverse engineering workflows by providing tools for browser observation, runtime sampling, hooking, debugging, network analysis, and local environment reproduction.

Enables Codex to rank available tools and skills, rerank code and documentation search results, and triage saved output artifacts using Jev-based relevance scoring while leaving final decisions to Codex.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

Enables MCP clients to open websites, capture screenshots at multiple breakpoints, and run automated design audits for issues like overflow, small tap targets, low contrast, missing alt text, tiny text, and clipped content.

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

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

Enables Codex to manage a local Claude Code companion through MCP, implementing a dual-agent workflow where Codex handles reasoning and review while Claude Code performs engineering tasks.

Analyzes Python code and provides guided refactoring suggestions without automatically modifying code.

Python MCP server for SolidWorks automation with 109 tools covering the full CAD lifecycle. Enables AI-assisted design workflows through COM automation on Windows.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

Enables Claude Code to control Firefox through tools for tabs, navigation, clicks, typing, scrolling, screenshots, page reading, forms, JavaScript, and console/network logs.

Executes Python code with safety constraints and manages Python packages through the Model Context Protocol.

Enables any MCP client to drive the user's real Chrome profile through a Chrome extension bridge, with tools for navigation, accessibility snapshots, ref-based interactions, console/network reading, screenshots, tab management, and restricted script evaluation.

Enables agents to review Python dependency requirements and source code against a modernization catalog, returning structured guidance, import-level suggestions, and compatibility floors. It also lists available codemod recipes and previews their transformations as unified diffs, all read-only and without installing packages or executing supplied code.

A Model Context Protocol server that extracts and analyzes Python code structures, focusing on import/export relationships between files to help LLMs understand code context.

Enables LLMs to run Python code and scripts across system and conda environments, list and install packages, and read or write text and binary files within an isolated working directory. It also provides prompt templates for generating, refactoring, and debugging Python code.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files securely within a configured working directory.

Enables LLMs to execute Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompt templates for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to run Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to work with local Python environments by listing environments and installed packages, installing dependencies, and running code or scripts. Also supports reading, writing, and listing files within a sandboxed working directory.

Enables LLMs to manage Python environments and packages, execute Python code or scripts, and read or write files within a secured working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to run Python code and scripts across available environments, manage packages, and read/write files within a secured working directory. It also provides prompts and resources for common Python development tasks.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes prompts for generating, refactoring, and debugging Python code.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files within an isolated working directory.