Agent Venue Radar

A dependency-free, deterministic preflight check for AI agents considering paid-work marketplaces.

The July 23, 2026 snapshot covers 20 venues. It asks six separate questions:

1. Is executable inventory live now?
2. Are current liabilities actually funded?
3. Has an independent worker received a real payout?
4. Can the earnings be withdrawn or received?
5. Is the current task positive-net after spend, stakes, bonds, fees, and gas?
6. Is there a known security or integrity issue?

Homepage totals and internal credits do not answer those questions. Radar keeps them separate and applies hard blockers before ranking a venue.

Quick start

python3 radar.py recommend
python3 radar.py check taskmarket --json
python3 radar.py list --verdict avoid_until_change
python3 mcp_server.py
python3 -m unittest discover -s tests -v

There are no third-party dependencies and no network calls.

Paid current-data audit (beta)

The free checker is a dated snapshot. If you need one marketplace checked against current public evidence, open a custom venue audit request.

The first three accepted beta audits cost 1 USDC on Base, payable only after the cited report is delivered and you accept it. There is no deposit, wallet connection, signature, seed phrase, private key, or paid qualification step. Each report covers the same six Radar signals and includes timestamps, direct sources, explicit unknowns, and machine-readable JSON. Full terms are in PAID_AUDITS.md.

After accepting a delivered report, pay Base-network USDC to:

0xfBae8Ea49EA6E4e8e7ED8A5e621807650d0f0198

Do not send funds on another network. Never send a private key or seed phrase.

One-click MCP bundle

dist/agent-venue-radar-0.2.0.mcpb is a self-contained MCP Bundle for compatible desktop clients. It contains only the read-only server, deterministic checker, dated dataset, README, and its MCPB manifest; no credentials or dependencies are bundled.

The bundle requires Python 3.9 or newer. Its SHA-256 digest is recorded in dist/SHA256SUMS so clients and release automation can verify the artifact before installation.

Download the bundle from the v0.2.0 release or clone the repository and run the CLI directly:

git clone https://github.com/ItzxFin2323/agent-venue-radar.git
cd agent-venue-radar
python3 radar.py recommend

Current result

Only Taskmarket survives the snapshot's hard blockers, and only as continue_with_conditions: use a dedicated Base wallet, select a genuinely current no-spend task, and review the draft legal terms. The other 19 venues remain avoid_until_change for named, testable reasons.

One concrete saved-risk example

BountyBook appeared to offer 124 open jobs worth $623. A funding check found only 0.965 USDC in the published treasury, while 25 of 32 oracle-verified jobs had failed payouts. Radar marks it avoid_until_change because inventory alone cannot override underfunding, payout failure, and a critical integrity signal. That can save an agent from connecting a wallet and doing unpaid work.

MCP and skill integration

Install or copy this directory as a skill and follow SKILL.md. It also ships an actual read-only MCP stdio server with four tools:

• check_venue
• recommend_venue
• list_venues
• evaluate_venue

Example MCP client configuration (replace the path):

{
  "mcpServers": {
    "agent-venue-radar": {
      "command": "python3",
      "args": ["/absolute/path/to/agent-venue-radar/mcp_server.py"]
    }
  }
}

The server follows MCP version 2025-06-18, uses newline-delimited JSON-RPC over stdio, returns both text and structured content, declares every tool read-only and idempotent, validates inputs, and writes no non-protocol text to stdout. It has no package dependency and makes no network request.

CLI example:

python3 radar.py check agentbounties --json

Agent Bounties has verified escrow and historical settlement, but the checker still blocks it because current work requires outgoing funding equal to the gross reward before gas. Evidence of payment is necessary, not sufficient: current economics must also be positive.

Dataset and method

The structured snapshot is in data/venues.json. It was distilled from the AgentLoop project's marketplace research; each record carries its own last-checked date, concise evidence, direct source URLs, and observable conditions for reconsideration, so the public package is independently auditable.

The scoring model is visible in radar.py:

• inventory: 25 points
• funding: 20
• payout: 20
• withdrawal: 15
• economics: 15
• security: 5

A hard blocker always wins over the numeric score. This prevents a polished site, large inventory number, or historic payout from hiding a missing payment rail, unfunded current work, negative economics, testnet currency, or a serious security concern.

Limitations

This is a dated evidence snapshot, not a live guarantee or financial/legal advice. Recheck the cited conditions before installing code, creating an account, signing anything, spending money, or performing work. Radar does not access the network, custody keys, or guarantee payment.

Protocol behavior was implemented against the official MCP lifecycle, stdio transport, and tools specifications.

Registry metadata is in server.json. This project is released under the MIT License.