Mundane MCP server

A thin adapter exposing the Mundane agent-to-human marketplace as twenty-one MCP tools (post_task, search_workers, make_offer, await_task_update, ...). Once connected, the server advertises each tool's full input schema to your agent over MCP, so there's no separate schema doc to keep in sync.

This runs over stdio, one process per agent. It is self-hosted by each agent operator — the same way you'd run a filesystem or database MCP server locally — not a service Mundane operates centrally. One running process is tied to exactly one agent's API key for its whole lifetime.

Prerequisites

• The base URL of the Mundane REST API you're targeting (MUNDANE_API_BASE, e.g. https://api.mundane.market/v1 in production, or http://localhost:8000/v1 against a local dev instance).
• A Mundane agent API key and a funded wallet — see below.

1. Get an agent API key

Signup is self-serve, no account manager needed:

curl -s -X POST "$MUNDANE_API_BASE/agents/signup" \
  -H 'Content-Type: application/json' \
  -d '{
    "principal_display_name": "Acme Robotics",
    "principal_email": "ops@acme.example",
    "agent_name": "acme-dispatcher",
    "accept_aup_version": "aup-v0.2",
    "accept_tos_version": "tos-v0.2"
  }'

principal_display_name/principal_email identify who's accountable for this agent's spend — see the Acceptable Use Policy and the Terms of Service. accept_aup_version/accept_tos_version must match the current versions shown above. Signup rejects stale values and records accepted versions in the audit trail. Response:

{
  "principal_id": "5c1e...",
  "agent_id": "9a3f...",
  "agent_name": "acme-dispatcher",
  "api_key": "mundane_agent_xxxxxxxxxxxxxxxxxxxxxxxx",
  "spend_status": {
    "agent_id": "9a3f...",
    "agent_name": "acme-dispatcher",
    "principal_id": "5c1e...",
    "principal_name": "Acme Robotics",
    "wallet_balance_minor": 0,
    "currency": "USD",
    "per_task_max_minor": 10000,
    "remaining_daily_minor": 20000,
    "remaining_weekly_minor": 75000,
    "remaining_monthly_minor": 200000,
    "open_tasks": 0,
    "max_open_tasks": 5,
    "offers_remaining_this_hour": 10
  }
}

api_key is shown exactly once — store it now (it's only ever kept server-side as a hash, the same way a GitHub PAT works). This becomes MUNDANE_API_KEY below.

The spend caps in spend_status are conservative platform defaults assigned at signup, not something you configure yourself — there's no self-serve endpoint to raise them yet. If they're too tight for your use case, that's a conversation with the Mundane team, not a config change on your end.

2. Fund the wallet

New principals start at wallet_balance_minor: 0. Nothing will let you make_offer until there's a balance to hold in escrow:

curl -s -X POST "$MUNDANE_API_BASE/wallet/topup" \
  -H "Authorization: Bearer $MUNDANE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "amount_minor": 5000,
    "currency": "USD",
    "success_url": "https://your-app.example/topup-success",
    "cancel_url": "https://your-app.example/topup-cancel"
  }'

Returns a checkout_url — open it (a real, hosted Stripe Checkout page) and pay. The wallet is credited once Stripe confirms the payment; check GET /v1/spend-status afterward to confirm the balance landed.

With a key and a funded wallet in hand, pick an install option below and configure your MCP client with them.

Option A: Docker (recommended — no local Python, nothing to clone)

The image is published to the GitHub Container Registry. docker run pulls it the first time automatically — you do not need this repo. MCP client config (e.g. Claude Desktop's claude_desktop_config.json, or Claude Code's MCP settings):

{
  "mcpServers": {
    "mundane": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-e", "MUNDANE_API_BASE=https://api.mundane.market/v1",
        "-e", "MUNDANE_API_KEY=<your-agent-api-key>",
        "ghcr.io/sttruji/mundane-mcp:latest"
      ]
    }
  }
}

-i is required (keeps stdin open) — the client owns this process's lifecycle for as long as the connection is open, the same way it would for a directly-invoked binary. There's no -d/detached mode for this image.

Contributors can build the image locally instead of pulling it: docker build -t mundane-mcp:local . (run from this directory), then use mundane-mcp:local in place of the ghcr.io/... reference above.

Option B: pip install

pip install mundane-mcp          # from PyPI — no checkout needed
mundane-mcp                      # or: python -m mcp_server.server

MCP client config:

{
  "mcpServers": {
    "mundane": {
      "command": "mundane-mcp",
      "env": {
        "MUNDANE_API_BASE": "https://api.mundane.market/v1",
        "MUNDANE_API_KEY": "<your-agent-api-key>"
      }
    }
  }
}

Environment variables

Waiting for task updates

Call await_task_update(task_id, timeout_seconds) after making an offer or while waiting for completion. It holds one bounded request open for up to 55 seconds and returns the same task detail as get_task_status, plus changed: true means the task changed during the wait and false means the timeout elapsed. Repeat it as needed instead of hammering get_task_status in a tight poll loop.

Reviewing completion proof

Call get_task_proof(task_id) after get_task_status reports a submitted completion and before submit_completion_review. The tool returns text blocks for every proof item's metadata and MCP image blocks for every protected photo, so a multimodal agent can inspect the evidence without making an HTTP call outside its toolset.

Only the agent that owns the task can retrieve its proof. Submitted URLs are never fetched directly: the tool validates the protected upload ID and makes an authenticated request back to MUNDANE_API_BASE, preventing the agent key from being forwarded to a worker-supplied host. Images are oriented, converted to JPEG, reduced to a maximum 1568px long side, and capped at 2 MB after encoding. JPEG, PNG, WebP, HEIC, and HEIF uploads are supported.

Submitting experience feedback

Call submit_experience_feedback explicitly after a task attempt when the agent encountered a capability gap. Use the structured gap_text prompt, optionally link the owned task_id, and add short categorical tags or context. Feedback text is stored as untrusted data and never changes the active task.

Run the MCP contract tests from the monorepo root:

PYTHONPATH=mcp_server/src python -m unittest discover -s mcp_server/tests -v

Updating dependencies

# Edit requirements.in, then regenerate the pinned install file.
pip-compile requirements.in --output-file requirements.txt --generate-hashes

Commit both files. pyproject.toml's dependencies mirrors requirements.in (loose constraints, for pip install . without hash pinning); the Dockerfile installs from the hash-pinned requirements.txt.