MantisBT MCP Server

MantisBT MCP Server

A Model Context Protocol (MCP) server that integrates the MantisBT REST API into Claude Code and other MCP-capable clients. Read, create, and update issues directly from your editor.

Requirements

• Node.js ≥ 18
• MantisBT installation with REST API enabled (version 2.23+)
• MantisBT API token (create under My Account → API Tokens)

Installation

Via npx (recommended):

Add to ~/.claude/claude_desktop_config.json (Claude Desktop) or your localclaude_desktop_config.json (Claude Code):

{
  "mcpServers": {
    "mantisbt": {
      "command": "npx",
      "args": ["-y", "@dpesch/mantisbt-mcp-server"],
      "env": {
        "MANTIS_BASE_URL": "https://your-mantis.example.com/api/rest",
        "MANTIS_API_KEY": "your-api-token"
      }
    }
  }
}

Local build:

git clone https://codeberg.org/dpesch/mantisbt-mcp-server
cd mantisbt-mcp-server
npm run init
npm run build

{
  "mcpServers": {
    "mantisbt": {
      "command": "node",
      "args": ["/path/to/mantisbt-mcp-server/dist/index.js"],
      "env": {
        "MANTIS_BASE_URL": "https://your-mantis.example.com/api/rest",
        "MANTIS_API_KEY": "your-api-token"
      }
    }
  }
}

Configuration

Environment variables

Config file (fallback)

If no environment variables are set, ~/.claude/mantis.json is read:

{
  "base_url": "https://your-mantis.example.com/api/rest",
  "api_key": "your-api-token"
}

Available tools

Issues

Notes

Attachments

Relationships

Monitors

Tags

Projects

Semantic search (optional)

Instead of exact keyword matching, semantic search understands the meaning behind a query. Ask in plain language — the search engine finds conceptually related issues even when the wording doesn't match:

• "login fails after password reset" — finds issues about authentication edge cases
• "performance problems on the checkout page" — surfaces related reports regardless of the exact terminology used
• "duplicate entries in the invoice list" — catches issues described as "shown twice", "double records", etc.

The embedding model (~80 MB) runs entirely offline — no OpenAI key, no external API. It is downloaded once on first start and cached locally. Issues are indexed incrementally on every server start (only new and updated issues are re-indexed).

Activate with MANTIS_SEARCH_ENABLED=true.

Which backend to choose?

Start with vectra. Switch to sqlite-vec if indexing or query times become noticeably slow.

npm install sqlite-vec better-sqlite3
# then set MANTIS_SEARCH_BACKEND=sqlite-vec

Metadata & system

HTTP mode

For use as a standalone server (e.g. in remote setups):

MANTIS_BASE_URL=... MANTIS_API_KEY=... TRANSPORT=http PORT=3456 node dist/index.js

# With token authentication and explicit bind address (required for Docker/remote):
# MCP_HTTP_TOKEN=secret MANTIS_BASE_URL=... MANTIS_API_KEY=... \
#   TRANSPORT=http PORT=3456 MCP_HTTP_HOST=0.0.0.0 node dist/index.js

Health check: GET http://localhost:3456/health (always public, no token required)

Development

npm run init         # First-time setup: install deps, git hooks, typecheck
npm run build        # Compile TypeScript → dist/
npm run typecheck    # Type check without output
npm run dev          # Watch mode for development
npm test             # Run tests (vitest)
npm run test:watch   # Run tests in watch mode
npm run test:coverage # Coverage report

License

MIT – see LICENSE

Contributing

Contributions welcome! Please read CONTRIBUTING.md.Repository: codeberg.org/dpesch/mantisbt-mcp-server