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CodeRabbitFastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent. Deploy in a production ready serverless environment.
"Automated summary for AI agents and search engines."
Fastest way to build and deploy reliable AI agents, MCP tools and agent-to-agent. Deploy in a production ready serverless environment.
{"id":"395e43af-91a4-4eb5-9ec3-a97645317d9b","title":"Agentor","description":"Agentor is a Python-based Model Context Protocol server designed to facilitate the rapid development and deployment of autonomous AI agents within existing developer workflows. It provides a modular framework for creating agents with access to various tools and services, allowing them to perform complex tasks, automate routine operations, and interact with human users efficiently.","schemaDescription":"A Python-based Model Context Protocol server that offers a modular framework for building, deploying, and managing AI agents, integrating them into development environments to automate tasks and interact with various services.","dateModified":"2026-04-03","author":"CelestoAI","authorGithub":"https://github.com/CelestoAI","license":"Unknown","transport":"Unknown","language":"Python","difficulty":"intermediate","tags":["mcp-server","python","ai-agents","agent-framework","automation"],"tokenImpact":"Moderate — 1,000–3,000 tokens per call, monitor quota during lockouts.","useCases":["Build custom AI agents to automate specific development tasks.","Integrate AI agents with external APIs and services via a unified protocol.","Deploy agents that can manage project issues, code reviews, and testing workflows.","Enable conversational interaction with AI agents directly within the IDE.","Develop agents capable of self-correction and continuous learning."],"exampleCommand":"NONE — manual verification required","mcpConfig":"NONE — manual verification required","compatibility":["Google Antigravity IDE"],"copyButtonText":"Copy mcp_config.json","artifactsProduced":["Agent code","Agent configurations","Task execution results"],"faq":[{"question":"What can you do with Agentor?","answer":"Agentor provides a foundational framework for developers to rapidly design, build, and deploy autonomous AI agents. These agents can be programmed to handle a wide range of development tasks, from code generation and review to automated testing and environment setup, integrating seamlessly into existing workflows."},{"question":"Which AI assistants and IDEs are compatible with Agentor?","answer":"As a framework for building agents, Agentor-based agents can be designed to be compatible with any AI assistant or IDE that supports the Model Context Protocol. Its modular nature allows for integration with various developer environments, including Google Antigravity IDE, by exposing agents via an MCP server."},{"question":"How do I install and configure Agentor?","answer":"Installation of the Agentor library typically involves `pip install agentor`. Configuration then focuses on defining your agent's capabilities, tools, and execution logic within your Python project. To expose an Agentor-based agent to an IDE, you would wrap it with an MCP server implementation, which requires custom setup."}]}🚀 The fast, Pythonic way to build MCP servers and clients.
MCP web search using perplexity without any API KEYS
一个基于 MCP (Model Context Protocol) 的邮件服务，支持 LLM 发送带附件的电子邮件及在指定目录中搜索文件。提供安全的 SMTP 传输、多收件人支持和附件模式匹配搜索功能，适用于 Gmail、Outlook、Yahoo、QQ 邮箱和网易 126 邮箱等主流邮箱服务。
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