"Guidance on operating Linux and installing services" matching MCP servers:

MCP Server for Git operations, agent templates, and project utilities.

A template for deploying an MCP server on Vercel, with example tools like roll_dice and get_weather.

Enables running a stateless Model Context Protocol server over Streamable HTTP on Cloudflare Workers or any fetch-capable runtime, with signed sessions, origin checks, and JSON-RPC request handling. It lets developers define tools with JSON Schema validation and per-request context.

Enables Claude Code, Cursor, and Codex users to orchestrate plan/build/review workflows across host or CLI models with a spec gate, tool vetting, hard per-run and per-day cost/token limits, safe defaults, and clear logs.

Enables AI assistants to interact with native Linux desktop applications through AT-SPI2 accessibility interfaces. Provides semantic element targeting, natural language search, and automation capabilities (clicking, typing, keyboard shortcuts) across GTK, Qt, and Electron applications.

A humorous MCP server that always advises against shipping on Friday, returning a catastrophic risk level and a rotating reason.

Governed development control plane for VedaOps projects, letting authorized agents inspect, search, patch, test, lint, and coordinate registered software without exposing a general shell.

MCP server deployed on EdgeOne Pages, providing a Streamable HTTP MCP server for intelligent chat applications with tool capabilities like generating webpages.

A comprehensive Model Context Protocol (MCP) server implementing the latest MCP specification with tools, resources, prompts, and enhanced sampling capabilities that features HackerNews and GitHub API integrations for AI-powered analysis.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Validates and tests llguidance grammars (Lark format), providing grammar validation, batch test execution, and syntax documentation.

Enables AI models to interact with IBM FileNet Content Manager for document management, folder operations, metadata handling, search, and content extraction through a standardized interface.

A deployment template for running Model Context Protocol servers as Vercel Functions. It provides a foundation for developers to build and host custom tools, prompts, and resources with optimized execution for MCP client integration.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

Hey @roocode community! I'm thrilled to share a project born from my work with Roocode and the vision of an AI-powered development team: the Anubis MCP Server! This system is heavily inspired by Roocode and designed to orchestrate an AI development workflow based on agile methodology. It simulates

This read-only MCP Server allows you to connect to SharePoint Excel Services data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out our MCP Server for SharePoint Excel Services (https://www.cdata.com/drivers/excelservices/download/mcp).

Enables LLM clients such as Cursor and Claude to discover and use generated REST services through MCP tools for create, update, and delete actions and MCP resources for list and get-by-id context. Services generated in one command are exposed simultaneously as REST endpoints and MCP tools and resources.

Enables AI assistants to discover and invoke CRUD tools and read-only resources over the Model Context Protocol for generated API services.

Exposes generated CRUD services to LLM clients over the Model Context Protocol, providing tools for create, update, and delete actions plus read-only resources for listing and fetching records by id. It lets assistants like Claude, ChatGPT, and Cursor discover and manipulate the same data served by the project's REST endpoints without any extra glue code.

Enables LLMs and MCP clients to discover and use generated CRUD services through MCP tools and resources, exposing actions like create, update, delete, and read-only list/get access alongside REST endpoints.

Enables AI assistants to discover and invoke generated CRUD tools and read-only resources, letting them interact directly with REST-backed services built on Express, Mongoose, or Sequelize.

Enables LLMs and AI assistants to discover and use tools and resources for CRUD operations on generated services, providing direct interaction with the same data exposed by REST endpoints.

Enables LLMs to discover and invoke CRUD tools and read-only resources for generated services, allowing direct interaction with the same data exposed by the REST API.

Enables LLMs to interact with generated REST API services by exposing MCP tools and resources for CRUD operations, listing, and retrieval. It lets AI clients discover and call create, update, delete, list, and get-by-id actions against the same data available over REST.

Enables LLMs to interact with generated CRUD services through MCP tools and resources, so they can create, update, list, and fetch records via a standard protocol. It also provides REST API access from the same codebase for traditional clients.

Enables LLM clients such as Claude, ChatGPT, and Cursor to discover and act on generated CRUD services over HTTP through tools like create, update, list, and delete, plus read-only resources for listing and fetching records by ID, with no extra glue code.

This MCP server provides read-only Linux system diagnostics tools for inspecting system information, processes, and log snapshots. It enables AI models to analyze Linux system health, troubleshoot issues, and review security through workflow prompts and HTTP transport with API key authentication.

Enables LLMs to discover and use generated REST API services as Model Context Protocol tools and resources, supporting CRUD actions and read-only data access without custom glue code.

Enables management of Windows servers from Linux through an MCP server with per-user installation. Provides tools to control Windows systems via API with secure credential management.

Enables LLMs to interact directly with generated REST services by discovering and invoking CRUD tools and reading exposed resources for list and get-by-id data.