What dependencies are needed to run the server?
Python 3.x with FastMCP, httpx, SQLAlchemy, Pydantic, and tenacity. Docker is recommended for running the Wiki.js instance itself.
@BehindUAll
Basic information
Model Context Protocol (MCP) server for Wiki.js with hierarchical documentation & Docker setup
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.
A Model Context Protocol (MCP) server that integrates AI assistants with Wiki.js to create, manage, and synchronize hierarchical documentation across multiple repositories. Designed for organizations with large-scale documentation needs.
Clone the repository, configure environment variables in .env, start Wiki.js via Docker (docker-compose -f docker.yml up -d), and run ./setup.sh to install dependencies. Then either start the MCP server with ./start-server.sh or configure it in Cursor by adding the command to ~/.cursor/mcp.json.
Python 3.x with FastMCP, httpx, SQLAlchemy, Pydantic, and tenacity. Docker is recommended for running the Wiki.js instance itself.
You can use a JWT token (API key from Wiki.js admin panel) or provide username/password credentials in the `.env` file.
It uses a local SQLite database (`wikijs_mappings.db`) for file‑to‑page mappings and repository context information.
Add the `start-server.sh` command to your `~/.cursor/mcp.json` file, then click the refresh icon in Cursor to load all 21 tools.
No explicit limits are mentioned; the system is designed for enterprise scalability handling hundreds of repositories and thousands of files.

The official Semantic-UI-React integration
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 162 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

Model Context Protocol Servers

Orcha is a unified context layer for AI tools and agents. It stores and indexes and exposes organizational, team, or individual knowledge. Files, structured databases, and connected sources live in one workspace, and age

Search saved employee handbooks from your AI assistant. Four read-only tools list authorized handbooks, browse policies, search content, and read source pages with citation links. Uses scoped bearer tokens and Streamable
Comments