VetAgent
A pre-trade safety check for AI agents. Before an agent buys, holds, or recommends
a token, it calls VetAgent and gets an actionable verdict — low / medium / high /
unknown — plus the specific signals behind it, instead of a wall of numbers to interpret.
Remote MCP endpoint: https://vetagent.dev/mcp · Landing page: https://vetagent.dev
// assess_token_risk("0x…", chain_hint="ethereum")
{
"risk_level": "medium",
"risk_score": 36,
"confidence": "high",
"signals": [
{"severity": "ok", "name": "流动性充足", "category": "liquidity"},
{"severity": "ok", "name": "可正常买卖", "category": "honeypot"},
{"severity": "warn", "name": "合约闭源", "category": "contract"}
],
"recommendation": "中等风险:需人工核实流动性、持币分布与合约权限后再决定。"
}
unknown is not low
This is the single most important thing to know about the output.
VetAgent is fail-closed. When a check cannot run — an upstream is down, a
buy/sell simulation fails, no liquidity data comes back — it returns unknown and
lists exactly what was missing in evidence.data_gaps. It never substitutes an
optimistic middle value, and it never sizes a position for you.
confidence measures how complete the input data was, not how safe the token is.
A risk tool that honestly says "I don't know" is useful. One that guesses is not.
Tools
| Tool | What it does |
|---|---|
assess_token_risk(address, chain_hint?, verbose?) | Full risk profile: sellability simulation, liquidity depth, pair age, holder concentration, contract permissions, upstream aggregate verdicts |
get_token_liquidity(address, chain_hint?) | Liquidity snapshot for the primary pair, with an explicit status so "upstream failed" is distinguishable from "no pools exist" |
find_new_hot_pools(chain?, limit?) | Newest / hottest pools on a chain. Discovery only — not a safety endorsement |
Pass chain_hint whenever you know it. Ethereum forks such as PulseChain inherit
contract addresses, so the same address exists on multiple chains with wildly
different prices; the hint removes that ambiguity. (Without it, VetAgent prefers
canonical chains — see _CHAIN_RANK in src/risk.py.)
Full agent-facing contract: docs/AGENT-INTEGRATION.md.
Accuracy benchmark
Most token-risk tools publish a feature list. We publish our error rates and the method behind them — including the parts that do not work yet — against labels produced by data sources the engine does not read.
What is measured today: a 11.3% false positive rate on 97 healthy tokens, a 21.0% unknown rate, and 6.7% of legitimate centralised assets flagged high.
What is not measured today: recall. Sampling turned up one dead token in 205, because every public source ranks by liquidity and rugged pools fall off the listing entirely. Rather than compute a detection rate on a single sample and present it as a result, that figure is left blank until the daily snapshot archive matures enough to supply a real cohort. An earlier version of this section claimed "measured recall", which the benchmark file itself contradicted.
→ bench/results.md — current numbers, method, and known limits.
Why it is built this way:
- Independent labels. If the benchmark labelled tokens using honeypot.is — which the engine reads — it would only measure whether VetAgent can relay honeypot.is. Labels come from realized market outcome (price/volume history) and from GoPlus, which is deliberately held out of the engine.
- A runtime assertion, not a promise. The benchmark records every endpoint each side touched and fails the run if the two sets intersect. A circular benchmark is worse than none, so it is made structurally impossible rather than documented.
- An ablation column. A token that already collapsed has ~zero liquidity today, so flagging it is close to tautological. Results are therefore reported twice: with all signals, and with liquidity/lifecycle signals removed. The gap is what the engine actually contributes beyond the obvious.
unknownrate reported alongside recall. A tool that answersunknownto everything has perfect recall and zero value.
python bench/build_dataset.py --limit 250 # sample + label (independent sources)
python bench/run_benchmark.py # score the local engine, write results.md
Tests
python tests/test_risk.py # engine regressions, offline, real upstream snapshots
python tests/test_mcp.py # MCP protocol conformance
python tests/test_upstream_contract.py # live: asserts the JSON paths we depend on still exist
Every case in tests/ is pinned to a defect that actually reached production.
The contract test earns its keep: VetAgent's worst bug was reading
simulationResult.isHoneypot when honeypot.is puts that flag in honeypotResult.
The key did not exist, the lookup returned None, it was read as False, and the
honeypot check silently passed every token it was ever asked about. No mocked test
could have caught that — only one that calls the real API and asserts the shape.
Rule for this repo: a commit that claims to fix something ships with a test that was red before it.
Architecture
Cloudflare Python Worker, no heavy dependencies.
src/
entry.py HTTP routing (entry class must be named Default)
risk.py risk engine — assess / liquidity / new_pools
mcp_server.py hand-written streamable-http MCP endpoint (JSON-RPC 2.0)
landing.html landing page
bench/ accuracy benchmark (independent labels + ablation)
tests/ regression, protocol, and upstream-contract suites
docs/ agent integration guide, handoff, MCP registry manifest
The MCP endpoint is hand-written rather than using the official mcp package: that
package pulls in pydantic's C extensions, which do not install on Cloudflare Python
Workers. Plain JSON-RPC turned out to be smaller and fully client-compatible.
Data sources
| Source | Used for |
|---|---|
| DexScreener | pairs, price, liquidity, volume, pair age |
| GeckoTerminal | liquidity fallback, new/trending pools |
| honeypot.is | EVM buy/sell simulation, taxes, aggregate risk, contract openness |
| RugCheck | Solana rug score, mint/freeze authority, holder concentration |
GoPlus is not used by the engine — it is reserved as the benchmark's held-out oracle. Adding it to the engine requires giving the benchmark a new independent labeller first, or the accuracy numbers stop meaning anything.
Where things are written down
Four documents, one job each. If something is in two of them, one of them is wrong.
| Document | Answers |
|---|---|
README.md (this file) | What is this, how do I call it |
docs/DECISIONS.md | Why is it built this way, and what enforces each rule |
docs/HANDOFF.md | Where things stand, what breaks, what to do next |
docs/STRATEGY.md | Who pays, what the moat is, when to shut it down |
Why a change was made lives in the commit message, which is immutable and attached to the diff. Why a line of code looks odd lives in a comment next to that line. Neither gets copied into a document, because a copy rots without anyone noticing.
Scope
VetAgent reports observable on-chain risk. It is not financial advice, it does not
size positions, and it cannot see off-chain risk — team behaviour, social engineering,
or a rug executed through governance. Treat low as "no fatal signal found in the
checks that ran", never as "safe to buy".
License
Not yet chosen — see docs/HANDOFF.md.