"Resources to Improve AI Coding Ability in C++ and Rust" matching MCP servers:

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

Captures user corrections and improvements during interactions, logs them, finds recurring patterns, and prescribes preventive fixes like CLAUDE.md rules, skills, scripts, or MCP tools to avoid repeating mistakes.

Web scraper and token compressor that converts HTML to clean markdown with 70-80% fewer tokens. Single-page compression and multi-page BFS crawling with auto-fallback fetch modes.

Enables MCP clients to run local scans for known AI coding-assistant backdoor indicators and risky Claude Code/MCP configuration patterns, returning findings directly in chat.

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.

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

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

MCP server for coding-os, a cognitive operating system that gives AI agents memory, structure, and discipline. It exposes tools for project management, task tracking, knowledge graphs, and cognition traces to Claude Code and Codex.

Enables external AI clients to invoke an autonomous coding agent that plans, fixes, and self-corrects code in an isolated Docker sandbox via run_task and run_tests tools.

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

Provides AI coding assistants with code search, impact analysis, task chaining, and persistent memory to turn AI demos into deliverable code.

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.

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.

Enables safe, AI-driven CATIA V5 modeling through 36 guarded tools for creating and inspecting parametric CATParts, including sketch, part design, GSD/surface, and transaction operations, while preventing mutation of existing documents.

Provides safe repository operations, skill reading, command execution, and test detection for coding agents over stdio.

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.

Provides intelligent semantic code search using local AI embeddings, enabling natural language queries to find relevant code by meaning rather than exact keywords. Indexes codebases in the background with smart project detection and privacy-first local processing.

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.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables supervising and orchestrating local coding agents like Muse and AGY through a client-agnostic MCP interface, with isolated Git worktrees, safe execution, and deterministic verification.

Enables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.

Enables AI agents to inspect UI screen recordings as token-efficient image sheets with changed regions boxed, single-frame flicker flagged, and speech aligned to frames. Exposes watch_video and zoom_video tools so agents can pull dense keyframes across any time range or one sharp full-resolution frame, all processed locally.

Enables AI coding assistants to search a local codebase semantically using AI embeddings, finding relevant code by meaning instead of exact keywords. It also provides package version lookup, manual reindexing, cache clearing, workspace switching, and server health status tools.

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.

Enables AI coding assistants to search a codebase by meaning using locally generated embeddings, so relevant code is found even when terminology differs from the query, along with package version lookup, reindexing, cache reset, workspace switching, and status tools. Indexing runs progressively in the background with CPU throttling and a SQLite cache, letting searches work immediately on large projects.

Enables AI agents to develop within a local project workspace by reading and modifying files, running commands and tests, checking Git state, and persisting progress as history sessions that can be restored in later conversations.