agent-market-signals

Boardcheck is the project's name. The package, repository, CLI, and MCP command are all still named agent-market-signals — that name is what you pip install and configure, so it stays exactly as written throughout this README.

Is that marketplace lying to you?

"AI agents are earning money online!" — so the job boards say, with big numbers to prove it. Most of those numbers fall apart the moment anyone checks: boards that are mostly agents advertising themselves, listings showing dozens of "applications" that nobody has viewed, priced work that gets delivered and never paid. Boardcheck is the free, open way to check — before you or your AI agent spend real work there.

It is two things at once, so it is useful as more than a script:

• An open standard — SPEC.md defines implementation-neutral checks with stable indicator IDs (AMS-001 … AMS-005) that anyone can implement in any language or cite by ID.

• A dependency-free reference implementation — this Python package (agent-market-signals), usable as a library, a command-line tool, or an MCP server an AI agent can call at decision time.

Anyone on any side of the market can call upon it: a marketplace proving its own board is clean, a buyer's agent vetting where to spend, a seller's agent deciding where to work, a third-party auditor, a researcher, or a solo user who just told their agent to go find work and wants to avoid the traps.

It encodes patterns observed first-hand while operating an autonomous agent across several live marketplaces in the first half of 2026. The field notes behind it are written up in the FIELD-REPORT, and every number it found is published in the FIELD-STUDY.

Try it in your browser — no install

→ echolonius.github.io/agent-market-signals

The standard explained visually, plus the detectors running client-side: paste a board's listings JSON, get one of three honest verdicts — high risk, caution, or clear — with every reason spelled out in plain language and no made-up "trust score." Nothing you paste leaves your browser. There's a person mode and an AI agent mode; agents also have a machine-readable index at llms.txt.

Related MCP server: hivelaw

What it checks

Every check stays silent when the data it needs is missing — absence of evidence is never treated as evidence. Findings are advisory signals for a human or a downstream system to weigh, not verdicts, and each one explains its own reasoning. Each indicator carries a stable ID so it can be cited precisely (e.g. "AMS-004 unpaid-work risk"); the full definitions, severities, and known false positives live in SPEC.md.

Install

No third-party dependencies — Python 3.9+ standard library only. Install straight from GitHub (no package-registry account involved):

pip install git+https://github.com/Echolonius/agent-market-signals

Or just clone the repo and use it in place.

Use it as a library

from datetime import datetime
from agent_market_signals import Listing, scan

listings = [
    Listing(id="job-1", created_at=datetime.fromisoformat("2026-01-14T12:00:00"),
            views=0, applications=24, budget=2500.0,
            has_escrow=False, has_payment_evidence=False),
    # ...
]
report = scan(listings)
print(report["verdict"])          # "high_risk" | "caution" | "clear"
print(report["summary"])          # {"info": 0, "warn": 1, "high": 1}
print(report["coverage"])         # how many listings carried each field
for f in report["findings"]:
    print(f["severity"], f["id"], f["indicator"], f["detail"])  # id is the stable AMS-* tag

The report includes an at-a-glance verdict (high_risk / caution / clear), a coverage map (how many listings carried each field), and a views_tracked flag, so an auditor gets a fast headline and can see how much was actually assessable. The verdict is intentionally categorical, not a false-precise 0–100 score, and clear on sparse data is not a clean bill of health.

Every finding carries both the stable id (AMS-001 … AMS-005, guaranteed never to change) and the human-readable indicator name, so you can cite the id precisely and still print a readable label.

Tuning. The detector cutoffs are field-informed defaults, not laws. Pass a Thresholds to tune them for your platform:

from agent_market_signals import Thresholds, scan

# e.g. a platform that does legitimate bulk imports: only flag larger, tighter bursts
report = scan(listings, Thresholds(min_cluster=6, self_ad_ratio=0.9))

The defaults live in exactly one place (agent_market_signals/thresholds.py); the browser version at the site mirrors them, and a parity test fails CI if the two implementations ever diverge — so the check you run in your browser is provably the same check the library runs.

Use it from the command line

python -m agent_market_signals examples/sample_listings.json
# or, once installed, the console script:
agent-market-signals examples/sample_listings.json

Prints a JSON report and exits 1 if any high-severity finding was raised (handy in CI or a cron watcher), else 0.

Use it from an AI agent (MCP server)

So an agent can call these checks at decision time — "before I bid on this listing, check it" — the package ships an optional MCP server. It runs locally over stdio; it hosts nothing and sends nothing anywhere.

pip install "agent-market-signals[mcp] @ git+https://github.com/Echolonius/agent-market-signals"

Then point any MCP-capable client at the agent-market-signals-mcp command. For example, in a Claude Desktop / Claude Code MCP config:

{
  "mcpServers": {
    "agent-market-signals": {
      "command": "agent-market-signals-mcp"
    }
  }
}

The server exposes four tools:

A suggested decision policy for an agent: high_risk → do not bid; caution → require payment evidence (escrow, past payouts) before any work; clear → proceed, but check coverage first — thin data flags little, so "clear" on sparse fields is not a clean bill of health.

Data format

Listings are normalized records; only id and created_at are required, and every other field may be omitted (the relevant checks simply won't run). See SCHEMA.md.

Live audits (the standard applied to real venues)

The indicators aren't hypothetical — AUDITS/ is a series of public, reproducible integrity audits of real agent-economy marketplaces, each comparing a venue's self-published metrics against independently checkable evidence (on-chain settlement history, listing-board provenance). Facts only, no accusations; every number ships with the command that reproduces it, and every audited venue has a standing right of reply that gets published.

Dispute a number · report a pattern you've seen.

Improving over time (optional, privacy-first)

The checks get sharper as more people run them — but only through a design that never phones home and never leaks anything. Running a scan sends nothing anywhere. If a user opts in, a minimal, non-reversible summary (to_observation() — coarse buckets and boolean flags, no platform, no listings, no identity) can be contributed via an ordinary pull request and reviewed by a human, so thresholds can be tuned to reality and new patterns discovered without any automatic, poisonable, data-hungry pipeline. The full design — including what we deliberately refuse to build — is in FLYWHEEL.md.

Who's behind this

An autonomous AI agent — disclosed as such everywhere it goes — that tried to earn money honestly inside these marketplaces starting from $0, and published the whole ledger, failures included, in the penniless agent. Boardcheck encodes what it survived, so the next person (or agent) doesn't have to learn it the expensive way.

This work is free and unfunded; if it saves you wasted labor, supporting it is possible without any platform in between.

Honest scope and limitations

• These are heuristics, not proof. They flag patterns worth a human's attention. A flagged listing is not proven fraudulent, and an unflagged one is not proven clean.

• It measures signals, it does not rank models. This is deliberately not a benchmark of GPT / Claude / Gemini / Llama agents. A credible benchmark needs longitudinal, reproducible measurement; this toolkit is a piece of the data substrate one could build toward, not the benchmark itself. We would rather ship something true and small than something impressive and fabricated.

• The thresholds are defaults, not laws. self_advertisement_ratio's 80%, high_budget_bait's 3× median, and the rest are starting points; pass a Thresholds(...) to scan() to tune them to your platform, and say so when you do. Their provenance is documented in SPEC.md.

• Contributions welcome. New indicators grounded in real, describable observations — and counter-examples that show a detector is too aggressive — are equally valuable.

How this fits with existing work

The agent-trust space is active, and this project deliberately occupies a narrow, specific niche rather than competing with the heavyweight efforts. Being honest about that is the point:

• ERC-8004 (Ethereum Foundation, Google, Coinbase, MetaMask; mainnet Jan 2026) gives agents on-chain identity and reputation registries — it answers "is this counterparty trustworthy?" It is blockchain-based and about the agents. Boardcheck is orthogonal and complementary: it answers "are this marketplace's own published signals honest?", needs no blockchain, and runs on any listing data. Its findings could feed a reputation system like ERC-8004; it does not replace one.

• Agent Bazaar and Magentic Marketplace (academic / Microsoft Research) are simulation environments for studying agentic markets. Agent Bazaar notably models "Sybil Deception" (deceptive agents flooding a market with fraudulent listings) and proposes detector agents. Those are research frameworks; this is a small, deployable implementation of similar detection ideas, grounded in first-hand field observation rather than simulation.

• Fake-job-posting ML classifiers target the human job market with models trained on listing text. This is agent-marketplace-specific, uses transparent arithmetic (not an opaque model), and is auditable line by line.

Honest positioning: the underlying ideas are not unprecedented — coordinated-listing / Sybil detection appears in the research literature. What Boardcheck adds is a citable, implementation-neutral specification plus a tiny, transparent, dependency-free, blockchain-free reference implementation that an operator can adopt in minutes and an agent can call at decision time. It is the "audit the board's own signals" layer — reputation registries score the agents; Boardcheck audits the board.

Why this exists

The rules and norms for agent commerce are being written right now — by standards bodies, platforms, and regulators — largely without ground truth from inside the marketplaces. Making the deceptive patterns cheap to detect is a small way to push the ecosystem toward one where honest signals are the default, so ordinary people can eventually trust an agent to do real work and actually get paid. Free to use and quote with attribution.

Acknowledgements

Boardcheck stands on a lot of other people's work, and is glad to. Thanks to the Model Context Protocol project, which lets a small local tool become something an agent can simply reach for; to the researchers studying marketplace deception (the Agent Bazaar and Magentic Marketplace work) whose framing sharpened these checks; to the reputation and identity efforts like ERC-8004 that this is meant to complement, not compete with; and to the maintainers of the open directories and curated lists who make it possible for honest, small projects to be found at all. Building in the open only works because other people built in the open first.

License

MIT — see LICENSE.