frisk-mcp
Frisk
Pre-transaction risk screening for autonomous AI agents.
Before your agent pays an x402 seller or calls an unfamiliar tool, ask Frisk
whether the counterparty is trustworthy and whether the transaction fits your
policy. Frisk returns a verdict — allow, review, or block — with a trust
score and human-readable reasons. It is advisory: your agent stays in control
of the decision.
import { Client } from "frisk-screen";
const client = new Client(); // lite mode, no key required
const result = await client.screen("0x9a3f1b2c3d4e5f60718293a4b5c6d7e8f9a0bc12", {
endpoint: "https://api.seller.x402/quote",
amount: 2.5,
asset: "USDC",
policy: { maxPerCall: 5.0 },
});
if (!result.allowed) {
console.log(result.verdict, result.trustScore, result.reasons);
}Surfaces
Surface | Package | Source |
TypeScript SDK |
| |
Python SDK |
| |
MCP server |
|
Both SDKs expose the same model: a Client with a screen() call, a lite
mode that runs locally with zero dependencies, and an optional hosted mode for
reputation history and live threat intelligence.
Related MCP server: hive-mcp-agent-kyc
MCP server
For agents that cannot import a library, and for asking the question
interactively, the same checks are available as an MCP server exposing one
tool, screen_payment:
{
"mcpServers": {
"frisk": {
"command": "npx",
"args": ["-y", "frisk-mcp"]
}
}
}No API key and no account: with no configuration it screens entirely on your
machine. It is listed in the MCP registry as dev.tryfrisk/frisk.
An MCP tool runs only when a model chooses to call it, so a check the model can
skip is a weaker guarantee than the same check on the code path that signs the
payment. Where the money actually moves, prefer the SDK. Details in
mcp/.
Lite mode vs. hosted
Lite (default) | Hosted (API key) | |
Runs | Locally, offline | Frisk API |
Signals | Public, structural checks only | Reputation graph, trained models, threat feed |
Confidence | Always | Rises with coverage |
Cost | Free | Usage-based |
Lite mode catches obvious problems — malformed counterparties, payTo swaps,
insecure endpoints, policy violations, and a small seed blocklist — without a
network call. The hosted API (https://api.tryfrisk.dev) adds reputation
history and continuously updated threat intelligence.
What a verdict covers
Frisk screens who you are paying. An allow means nothing disqualifying was
found by the checks you gave it enough information to run: the counterparty
parses as an address and is not on the blocklist, the payTo the endpoint
asked for matches the counterparty you named, the endpoint is served over
HTTPS, and the amount and asset fall inside the policy you supplied. A check
whose input you leave out does not run and does not fail — omit
observedPayTo and no payTo comparison happens. In hosted mode an allow
also means no adverse reputation history was found.
It says nothing about what comes back. Whether the response matches the shape you expected, contains the data you paid for, or is worth the price is a separate question, and Frisk deliberately does not answer it. Verifying the response contract is worth doing; it belongs after the call, on the buyer's side, against the buyer's own definition of a satisfactory answer.
An allow is not a claim of safety in general either. In lite mode confidence
is always low, because the checks are structural: a counterparty with no
history and no defects screens the same as one with a long clean record. The
verdict is one input to your decision, which is why it is advisory.
Design principles
Advisory, not in-path. Frisk never holds your funds or blocks a payment itself; it returns a verdict and your code decides.
Zero runtime dependencies. The TypeScript SDK is built on the platform
fetchAPI (Node, Bun, Deno, Workers, browser); the Python SDK uses only the standard library.Typed. Both SDKs ship with full type information.
Contributing
See CONTRIBUTING.md. Security disclosures: SECURITY.md.
License
The hosted API at api.tryfrisk.dev is additionally governed by the
Terms of Service.
Available Tools
1 toolscreen_paymentScreen a payment before payingARead-only
Screen a counterparty an agent is about to pay. Runs entirely offline: address sanity, dynamic-payTo comparison, transport check, and your spending policy. Returns allow / review / block with reasons. Advisory: the caller decides. Set FRISK_API_KEY to add hosted reputation signals.
| Name | Required | Description | Default |
|---|---|---|---|
| asset | No | Asset symbol, e.g. USDC. | |
| amount | No | Amount the agent is about to pay. | |
| endpoint | No | URL that quoted the payment. Used to check transport safety. | |
| maxPerCall | No | Spending ceiling for a single call. | |
| strictness | No | 0 is permissive, 1 is paranoid. Defaults to 0.3. | |
| counterparty | Yes | The address the agent intends to pay, e.g. an 0x EVM address. | |
| allowedAssets | No | Assets the agent is permitted to pay in. | |
| observedPayTo | No | The payTo address the endpoint actually returned for this request. If it differs from the counterparty, that is the dynamic-payTo swap. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it runs offline, returns a recommendation with reasons, leaves the final decision to the caller, and notes the optional FRISK_API_KEY for hosted signals. This goes beyond the annotations and fully describes behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that convey purpose, behavior, and optional configuration without any redundant or tangential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explicitly names the return categories (allow/review/block with reasons) and clarifies that it is advisory and offline, covering all essential context an agent needs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 8 parameters with clear descriptions. The description adds relational context (e.g., dynamic-payTo comparison between observedPayTo and counterparty, spending policy tied to allowedAssets/maxPerCall/strictness), enhancing the schema's meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the action (screen), the object (counterparty about to be paid), and the outcome (allow/review/block with reasons). No ambiguity about the tool's purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies when to use (before paying) and provides operational context (runs entirely offline, advisory only). Does not explicitly contrast with alternatives, but no sibling tools are present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.0.1- First observed
screen_payment
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is singular and clearly defined.
The single tool uses a clear verb_noun pattern ('screen_payment'), which is consistent and predictable.
One tool feels thin for a general-purpose server, but for a narrowly focused payment screening service, it may be sufficient. It's borderline on the calibration scale.
The tool covers the essential screening workflow (address checks, policy, etc.), but lacks auxiliary capabilities like configuration or reporting. Minor gaps that agents can work around.
Maintenance
Related MCP Connectors
Check an x402 endpoint before your agent pays it: avoid / caution / ok, proven on-chain.
Verify x402 payment endpoints before an AI agent pays: scam scan, on-chain checks, trust scores.
Entity verification, sanctions screening, and trust scoring for AI agents via x402 micropayments.
Crypto transaction firewall and risk tools for MCP agents.
Related MCP Servers
- AlicenseAqualityDmaintenancePre-flight oracle for x402 endpoints — gives any MCP-aware agent (Claude Code, Cursor, Windsurf, Continue) tools to detect decoys, zombies, and price traps before paying. Wraps preflight, forensics, decoy-blacklist, and watch-subscription endpoints from x402station.io.10233MIT
- AlicenseNot gradedqualityCmaintenanceRoutes blockchain address screening to third-party KYC/AML providers and checks public sanctions lists, serving as a clean-money gate primitive for MCP-compatible agents.MIT
- FlicenseNot gradedqualityDmaintenanceProvides a pre-signature payment-risk verdict (GO/HOLD/STOP) for x402 payments based on counterparty reputation, price anomaly, and OFAC sanctions.-
- FlicenseNot gradedqualityCmaintenanceProvides counterparty risk checks for any EVM address, returning malicious-history flags, tiered risk scoring, and plain-language summaries via x402 payment.-
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