"Information about Rust programming language or rust corrosion" matching MCP servers:

TypeScript-based MCP server designed to enhance code editing experiences by providing features such as hover information, code completion, and diagnostics.

Enables local, Docker-isolated code execution across six programming languages including Python, Rust, and TypeScript. It features pre-warmed container pooling, persistent sessions, and built-in support for machine learning libraries.

Enables users to query ClinicalTrials.gov using natural language instead of complex API parameters, translating plain English requests into structured API calls and returning organized clinical trial data.

MCP server that executes Blueprint workflows (flow.ir) with swarm operations, dispatching agent steps to backends like Lua, Rust, processes, or interactive Operator sessions.

A Rust-based MCP server that provides long-term memory capabilities for AI agents using keyword-based storage and retrieval. It supports isolated namespaces for different users or projects, allowing LLMs to remember and recall information across sessions.

Enables authenticated natural-language interaction with the CoSMeTIC zero-knowledge proof service by exposing tools to request proofs, check status, download proofs, and verify results, while managing job and session state.

Enables agents to get fast, calibrated probabilistic answers from Jev (Typesafe AI) to yes/no, scale, or choice questions about provided material, without using a generative model.

Durable, agent-native AI runtime with native MCP client and server support. Rust core for performance with Python SDK for workflow authoring. Features graph-based workflows, durable execution, A2A protocol support, and multi-agent coordination.

Enables LLM assistants like Claude to interact with Zaif cryptocurrency exchange through natural language, supporting market information retrieval, chart data, trading, and account management for BTC/JPY, ETH/JPY, and XYM/JPY pairs.

High-performance local MCP server and CLI written in Rust that scrubs 90%+ framework noise from AI debug logs with Tree-sitter AST validation and test rollback.

An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

Deterministic pre-execution safety certification for autonomous AI agents. Evaluates proposed actions across scope, reversibility, and sensitivity constraints — returns Certified, Warning, Escalate, or Blocked decisions with cryptographic audit trails. 3 tools: certify_action, check_budget, get_certification_history. Built on the QAE Safety Kernel (Rust + PyO3 bindings, pip install qae-safety).

Local-first persistent memory for Claude Code and OpenAI Codex CLI. A single Rust binary captures, distills, searches, and injects project memory across sessions via hooks, MCP, CLI, and localhost REST.

Autonomous LLM-powered web crawler with 17 browser tools and goal-driven agent. Single Rust binary with stealth browsing, provider-agnostic LLM support, and built-in MCP server.

Judgment calls on small, fast, cheap models: yes/no, pick-one and rating questions about any text, answered YES, NO or UNSURE. Four tools (likely, classify, rate, each) for TypeSafe's Jev, open models on Ollaya, Ollama or a local NLI model.

Gives an LLM agent control of the macOS desktop from a single self-contained Rust binary: Set-of-Mark numbered screenshots, Accessibility-tree clicks with browser-JS fallback for web content, Apple Vision OCR, mouse, keyboard, and clipboard.

Enables AI agents to perform step-through debugging of Python, JavaScript/Node.js, and Rust programs using the Debug Adapter Protocol, with support for breakpoints, variable inspection, and stack traces.

Transforms unstructured natural language operational problems into rigorously formulated, validated, and solved Mixed-Integer Linear Programming (MILP) models via an autonomous multi-agent graph, bringing closed-loop Operations Research capabilities to LLM assistants and MCP clients.

A Model Context Protocol server that converts Markdown content into interactive mindmaps, allowing AI assistants to visualize hierarchical information through either HTML content or saved files.