Check if AI-powered features like RAG-based Q&A, semantic search, and document summarization are enabled and view index statistics for the Outline wiki.
Search and retrieve Confluence content using CQL queries via the MCP Atlassian Server, enabling efficient access to Confluence data within the MCP interface.
Search Atlassian Confluence content using CQL or basic text queries with filters for title, space, labels, and content type. Includes pagination and returns Markdown-formatted results with snippets and metadata.
Generate text using Google's Gemini AI models with customizable parameters like temperature, token limits, and optional features including JSON mode, Google Search grounding, and conversation context.
Provides intelligent summarization capabilities through a clean, extensible architecture. Mainly built for solving AI agents issues on big repositories, where large files can eat up the context window.
A server that enables AI models to interact with Confluence Data Center through REST API, providing operations like searching, reading, creating, updating, and deleting pages.