"Using Claude in Cursor to Run and Read Outputs from PowerShell or Terminal" matching MCP servers:

Enables agents to discover, evaluate, and serve task-specialist models (embeddings, reranking, classification, extraction) with OpenAI-compatible endpoints and MCP tools for routing, embedding, reranking, and extraction.

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

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.

Enables web search, content extraction, question answering, and RAG context generation using Tavily API with Cursor-compatible underscore-named tools.

Enables cost-effective repository analysis, code search, file editing, and task planning by wrapping the cursor-agent CLI through focused tools. Reduces token usage by offloading heavy thinking tasks from Claude to specialized operations with configurable output formats.

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

Enables searching through vectorized Cursor IDE chat history via a FastAPI service powered by LanceDB and Ollama.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

A persistent semantic memory system for Claude Code that provides a structured, versioned document store with semantic search and graph visualization. It acts as a memoization layer to store and retrieve research, design decisions, and codebase insights across different work sessions.

Multi-agent AI consultation framework for Claude Code via MCP — get second opinions from OpenAI, Gemini, Qwen, DeepSeek

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Enables structured team communication for Claude Code agents through Slack-like channels and direct messages. Supports project isolation, subscription management, and agent notes for sophisticated multi-agent collaboration workflows.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Enables Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.

Enables Claude Code to search and retrieve information from its past session transcripts, including tool calls, errors, and project-specific history, via an SQLite FTS5 full-text search index.

Enables Claude Code to use local markdown notes as persistent, searchable memory with hybrid recall, episodic memory, note-graph traversal, design memory, and optional cloud sync.

Enables searching and retrieving Claude Code conversation history via hybrid semantic and keyword search, allowing the agent to access its own past interactions.

Cross-machine context sharing MCP server for Claude Code, enabling shared memory of decisions, patterns, and knowledge across multiple Claude Code instances on different machines.