What environment variables are required?
The server requires `ATLASSIAN_API_TOKEN`, `CONFLUENCE_URL` (e.g., `https://<your-domain>.atlassian.net/wiki/api/v2`), and `CONFLUENCE_EMAIL`.
@7nohe
Basic information
Confluence APIを使用したMCP Serverのサンプル実装です。
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryTool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
It is a sample Model Context Protocol (MCP) server implementation that uses the Confluence API to retrieve and create Confluence pages. It is intended for developers building MCP-based integrations with Confluence.
Install dependencies with npm install, then build the project with npm run build (or npm run watch for development). After build, configure your MCP client (e.g., Claude Desktop) with the absolute path to dist/index.js and set the required environment variables: ATLASSIAN_API_TOKEN, CONFLUENCE_URL, and CONFLUENCE_EMAIL.
—
The server requires `ATLASSIAN_API_TOKEN`, `CONFLUENCE_URL` (e.g., `https://<your-domain>.atlassian.net/wiki/api/v2`), and `CONFLUENCE_EMAIL`.
Run `npm install` to install dependencies, then `npm run build` to compile the TypeScript code. The output script is at `dist/index.js`.
Add an entry to Claude Desktop’s `mcpServers` configuration with the absolute path to the built script and the required environment variables as shown in the README example.
It uses the Confluence API to get existing Confluence pages and create new pages.
The server requires Node.js and npm. It also relies on a valid Confluence API token and email for authentication.
Person-owned, portable AI memory served over MCP. Your knowledge, preferences, and decisions — captured in one assistant, available in all of them.

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,
Shared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.

Serve your docs to AI agents over SSH and MCP. Agents use ls, cat, grep, find, and pipes against a virtual filesystem backed by your documentation. Identity-scoped access, governed writing with human approval, and portab
Groundwork: company memory for your AI tools. Docs public. Source closed. Public proof needs no account.
Comments