Parallel Task MCP provides a way to initiate and manage research or task groups through LLM clients using the Model Context Protocol. It enables seamless integration with Parallel’s APIs for flexible experimentation and production development. The tool supports both remote and local deployment, and offers connection capabilities for context-aware AI workflows.

• ⭐ 4
• MCP
• parallel-web/task-mcp

Teamwork MCP Server is an implementation of the Model Context Protocol (MCP) that enables Large Language Models to interact securely and programmatically with Teamwork.com. It offers standardized interfaces, including HTTP and STDIO, allowing AI agents to perform various project management operations. The server supports multiple authentication methods, an extensible toolset architecture, and is designed for production deployments. It provides read-only capability for safe integrations and robust observability features.

• ⭐ 11
• MCP
• Teamwork/mcp

Weblate MCP Server enables AI assistants and clients to directly manage Weblate translation projects through the Model Context Protocol (MCP). It integrates with the Weblate REST API, allowing natural language interaction for project and translation management. The tool offers multiple transport options including HTTP, SSE, and STDIO, and is optimized for large language model workflows. Full support for project, component, and translation operations is provided, with a focus on type safety and flexible environment configuration.

• ⭐ 9
• MCP
• mmntm/weblate-mcp

tavily-search MCP server provides an MCP-compliant server to perform search queries using the Tavily API. It returns search results in text format, including AI responses, URLs, and result titles. The server is designed for easy integration with clients like Claude Desktop or Cursor and supports both local and Docker-based deployment. It facilitates AI workflows by offering search functionality as part of a standardized protocol interface.

• ⭐ 44
• MCP
• Tomatio13/mcp-server-tavily

Scrapeless MCP Server is a powerful integration layer enabling large language models, AI agents, and applications to interact with the web in real time. Built on the open Model Context Protocol, it facilitates seamless connections between models like ChatGPT, Claude, and tools such as Cursor to external web capabilities, including Google services, browser automation, and advanced data extraction. The system supports multiple transport modes and is designed to provide dynamic, real-world context to AI workflows. Robust scraping, dynamic content handling, and flexible export formats are core parts of the feature set.

• ⭐ 57
• MCP
• scrapeless-ai/scrapeless-mcp-server

MCP-searxng enables agentic systems to interface with web search engines via the SearXNG platform by implementing the Model Context Protocol. It supports both command-line and local server deployment, providing flexible integration options. Users can configure custom SearXNG server URLs and connect through clients like uvx or claude desktop. The tool simplifies access to structured web search within agentic workflows.

• ⭐ 107
• MCP
• SecretiveShell/MCP-searxng