MCP Server · v0.1.0

Your AI agent,
inside Dhamark.

Connect Claude Code, Cursor, or Claude Desktop to Dhamark. Assign a ticket to the AI, and it picks it up, implements it, opens a PR, and marks it ready for QA — with no manual hand-off.

Get started →View tools
How it works
1
Assign to Dhamark
In the portal, set any ticket's assignee to "Dhamark" and status to "Todo". The MCP server will surface it.
2
Agent picks it up
The AI calls list_assigned_tickets → get_ticket → start_work, branches off suggested_branch, and implements the spec.
3
Auto-handed back
After gh pr create, the agent calls link_pull_request then mark_ready_for_qa — ticket goes back to you for review.
Setup
1
Generate an API key

In the Dhamark portal go to Settings → API Keys → Generate key. Copy the dmk_… key — it's shown only once.

Open Settings → API Keys →
2
Configure your AI host

Choose your AI coding host. The MCP server runs via npx — no global install needed.

Claude Code
Cursor
Claude Desktop

Run this once in your terminal:

claude mcp add dhamark -- npx -y @dhamark/mcp

Then set the env vars via /mcp or add them to .claude/settings.json.

▸ Show config for Cursor / Claude Desktop
Cursor

Create or update .cursor/mcp.json in your project root (or ~/.cursor/mcp.json globally):

{
  "mcpServers": {
    "dhamark": {
      "command": "npx",
      "args": ["-y", "@dhamark/mcp"],
      "env": {
        "DHAMARK_API_URL": "https://app.dhamark.com",
        "DHAMARK_API_KEY": "dmk_your_key_here"
      }
    }
  }
}
Claude Desktop

Edit claude_desktop_config.json (found in Claude Desktop settings):

{
  "mcpServers": {
    "dhamark": {
      "command": "npx",
      "args": ["-y", "@dhamark/mcp"],
      "env": {
        "DHAMARK_API_URL": "https://app.dhamark.com",
        "DHAMARK_API_KEY": "dmk_your_key_here"
      }
    }
  }
}
VariableRequiredDescription
DHAMARK_API_KEYyesPersonal API key starting with dmk_…
DHAMARK_API_URLnoBase URL of the Dhamark portal (defaults to http://localhost:3000)
3
Give your agent a system prompt

Paste this into your agent's system prompt or project instructions:

Use the dhamark MCP server. Call list_assigned_tickets to find todo work, get_ticket for full context, then start_work. Create a branch using the ticket's suggested_branch, implement the acceptance criteria and edge cases, open a PR with gh pr create, call link_pull_request with the PR URL, and finally mark_ready_for_qa.

The agent will then pick up any ticket assigned to Dhamark and work through it end-to-end.

Tools reference
list_assigned_ticketsread

Return all tickets assigned to Dhamark in a given status (defaults to "todo"). Start here to pick up work.

statusstring?Filter by status — e.g. "todo", "in_progress"
get_ticketread

Fetch full ticket context: description (markdown with acceptance criteria & edge cases), screenshot URL, spec, labels, and the suggested_branch the agent must use.

idstringTicket ID
start_workwrite

Move a ticket to "in_progress". Call this right after picking it up and before writing code.

idstringTicket ID
add_ticket_commentwrite

Post a progress note or update on the ticket. Markdown supported.

idstringTicket ID
bodystringComment body (markdown)
link_pull_requestwrite

Record the GitHub PR URL on the ticket after running `gh pr create`.

idstringTicket ID
urlstringPull request URL
titlestring?Optional PR title or identifier
mark_ready_for_qawrite

Set status to "qa", reassign the ticket back to the delegator, and post a "Ready for review" comment. Call this last, after the PR is linked.

idstringTicket ID
notestring?Optional extra note for the review comment
Notes
  • —stdout is reserved for the MCP JSON-RPC protocol — all diagnostic logs go to stderr.
  • —API keys are stored as SHA-256 hashes only. To revoke, go to Settings → API Keys.
  • —The MCP server never touches git or GitHub directly. The host AI agent runs those commands with its own tools.
  • —Set DHAMARK_API_URL to your deployed portal. Leave unset only for local dev where the portal runs on http://localhost:3000.