MCP Server

⚠️ Experimental — tool handlers currently return stub data. Full implementation tracked in #28.

Rungu includes a built-in MCP (Model Context Protocol) server that lets AI agents query and manage feedback directly.

Setup

Add to your MCP configuration:

Claude Code (.claude/settings.json)

{
  "mcpServers": {
    "rungu": {
      "command": "rungu",
      "args": ["mcp", "--db", "/path/to/rungu.db"]
    }
  }
}

Cursor / Windsurf

{
  "mcpServers": {
    "rungu": {
      "command": "rungu",
      "args": ["mcp", "--db", "/path/to/rungu.db"]
    }
  }
}

Available Tools

Tool	Description
list_projects	List all feedback projects
get_project	Get project detail by slug
list_posts	List posts with filters (status, category, sort)
get_post	Get post detail with comments
create_post	Submit a new feedback post
update_post_status	Change post status (open → planned → done)
vote_post	Toggle vote on a post
search_posts	Full-text search across posts
list_comments	Get comments for a post
add_comment	Add comment to a post
get_stats	Project stats (total posts, by status, by category)
get_trending	Top voted posts in last 7 days

Example Usage

In Claude Code:

"Show me all open bug reports with the most votes"
→ calls list_posts(status=open, category=bug, sort=most_votes)

"Create a feature request for dark mode in the my-saas project"
→ calls create_post(project_slug=my-saas, title="Dark mode support", category=feature)

"What's trending this week?"
→ calls get_trending()

Transport

The MCP server uses stdio transport (stdin/stdout). No HTTP server needed — it runs as a subprocess of the AI agent.

No Auth Required

Since MCP runs as a local subprocess, no authentication is needed. The agent has direct access to the SQLite database.