What deployment modes are supported?
Cloud (`cloud`) and Server/Data Center (`server`), set via `CONF_MODE`.
@qihaze123
Confluence MCP server for Cloud/Server/Data Center. Supports whoami, search, raw CQL, create and update page.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"confluence": {
"command": "npx",
"args": [
"-y",
"confluence-mcp-server@1.1.0"
],
"env": {
"CONF_MODE": "server",
"CONF_BASE_URL": "https://confluence.example.com",
"CONF_AUTH_MODE": "auto",
"CONF_USERNAME": "your-username",
"CONF_TOKEN": "your-token"
}
}
}
}Get current authenticated Confluence user (whoami).
Search Confluence pages by keyword. Returns unified fields: id, type, title, spaceKey, url, version.
Execute raw Confluence CQL search. Returns unified fields: id, type, title, spaceKey, url, version.
Get a Confluence page by ID. Returns unified fields and bodyStorageValue.
Create a Confluence page. Supports optional parentId. Returns unified fields.
Update a Confluence page content. Auto increments version. Returns unified fields.
An MCP server that integrates with Atlassian Confluence (Cloud and Server/Data Center), allowing AI agents to search, retrieve, create, and update pages via the Model Context Protocol.
Run it via npx with required environment variables: CONF_MODE (cloud or server), CONF_BASE_URL, CONF_AUTH_MODE (default auto), CONF_USERNAME, and CONF_TOKEN. Configure your MCP client with the command npx -y confluence-mcp-server@1.1.0 and the above env settings.
Cloud (`cloud`) and Server/Data Center (`server`), set via `CONF_MODE`.
Set `CONF_AUTH_MODE=auto` and provide your username and API token/password via `CONF_USERNAME` and `CONF_TOKEN`.
The server runs under Node.js and is executed via `npx`; no additional installation steps are needed if Node.js is available.
All data stays in your Atlassian Confluence instance; the MCP server only makes API calls to that instance based on user queries.

One permissioned folder your team and your AI agents both log into.

Model Context Protocol Servers

Local-first MCP server that snapshots your project's working state into one context object. Open-core, privacy-first.

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.
评论