all-in-one-model-context-protocol is a powerful Model Context Protocol (MCP) server implementation designed for AI-enhanced development workflows. It provides integrations with GitLab, Jira, Confluence, YouTube, Google Calendar, Gmail, and many other services, along with built-in practical tools such as AI search, RAG (Retrieval-Augmented Generation) memory, web scraping, script execution, and Deepseek reasoning.
Important Update: This project has been split and migrated to multiple independent repositories, including Google Kit (Gmail, Google Calendar, Google Chat tools), RAG Kit (RAG and memory tools), Dev Kit (developer tools such as Jira, Confluence, GitLab, GitHub, etc.), Fetch Kit (scraping and crawling tools), and Research Kit (research, academic, and reasoning tools). The original repository may no longer be maintained; it is recommended to visit the new repositories for the latest features.
Main Capabilities
- Calendar Management: Create, list, update, and reply to Google Calendar events.
- Confluence: Search, retrieve, create, and update pages.
- Deepseek Reasoning: Leverage Deepseek AI for multi-step problem solving and strategic decision support.
- Web Content Retrieval: Scrape text content from HTTP/HTTPS URLs.
- Google Chat: List spaces and send messages.
- Gemini Search: Use Google AI search to obtain real-time information.
- GitLab: Manage projects, merge requests, file contents, pipelines, commits, and user events.
- Gmail: Search emails, move to trash, create and manage filters and labels.
- Jira: Retrieve, search, create, and update issues, list sprints and statuses, and transition issues.
- RAG Memory: Index content or files, create, delete, and list collections, and search and delete indexes.
- Script Execution: Safely execute command-line scripts with sandboxing, timeout protection, and output capture.
- Brave Search: Perform web searches via the Brave Search API.
- Sequential Thinking: A dynamic reflective problem-solving tool that supports multi-step reasoning, hypothesis generation, and validation.
Usage
- Environment requirements: Go 1.23.2 or later, as well as API keys and tokens for the required services.
- Installation: Can be installed automatically via Smithery (interactive CLI setup), or manually using Go.
- Configuration: You need to create a .env file to configure environment variables, such as GOOGLE_TOKEN_FILE (for Google AI services such as Gemini) and GOOGLE_CREDENTIALS_FILE (service account, for advanced features such as Google Chat). You can also enable specific tool groups via the ENABLE_TOOLS environment variable (a comma-separated list of tool groups); leaving it empty enables all tools. Tool groups include: gemini, fetch, confluence, youtube, jira, gitlab, script, rag, deepseek.
Applicable Scenarios Suitable for development teams and individual developers who need to combine development tools such as GitLab, Jira, and Confluence with AI capabilities to achieve automated code review, issue tracking, document management, email processing, calendar scheduling, knowledge base retrieval, and intelligent search. It can also be used for AI application development that requires RAG memory and complex reasoning.
Limitations
- Before use, you must configure the API keys and tokens for the corresponding services. Some features (such as Google Chat) require creating a Google Cloud project and service account.
- Although the script execution feature is subject to security restrictions, it still needs to be used with caution.
- The project has been split into multiple independent repositories. The original repository may no longer be updated; it is recommended to use the new repositories for continued support.
- Specific details such as fees, pricing, and platform compatibility are not yet provided.
