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LayerCall

by com.layercall

Verify a signed AI agent (Web Bot Auth)

verify_agent

Cryptographically verify a Web Bot Auth signature (RFC 9421) — proof of WHICH agent is calling, not a guess from the user-agent. Returns verified true/false plus the agent's identity and declared purpose. This is the only check here that proves rather than infers, so it carries no score and no verdict: a verified assistant acting for a real user is usually welcome, a verified scraper usually is not, and that policy is the caller's. Pass the request the agent made to YOU — the signature covers its method, authority and path.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesAbsolute URL the agent requested from you
methodNoHTTP method, default GET
headersYesRequest headers, including signature, signature-input and signature-agent

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full responsibility. It discloses the return values (true/false plus identity and purpose), the absence of score/verdict, and the crucial requirement that the input must be the request the agent made to the caller. These are meaningful behavioral details beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is four sentences, front-loaded with the core purpose, then covering output, unique value, and invocation requirement. While slightly wordy, every sentence contributes useful information and the structure is logical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must explain return values, which it does clearly. It also covers the tool's place among siblings, the caller's policy role, and the critical input condition, making it complete for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% parameter coverage, so the baseline is 3. The description adds value by instructing the caller to pass the agent's original request and noting that the signature covers method, authority, and path, which clarifies how to set url and headers correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool cryptographically verifies a Web Bot Auth signature (RFC 9421), using a specific verb+resource. It distinguishes itself from sibling tools by emphasizing it is the only check that 'proves rather than infers,' making its purpose unique.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use this tool: when you need proof of agent identity rather than an inference from user-agent or scoring. It mentions that the policy decision is the caller's, but does not explicitly exclude alternatives or name specific sibling tools, leaving a small gap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation5/5

Each tool targets a distinct resource or action: phone, device, domain, IP, user (aggregate), agent, and email. The descriptions clearly separate individual signal scoring from the combined user scoring, and cryptographic verification is uniquely assigned to verify_agent. No two tools overlap in purpose.

Naming Consistency4/5

Most tools follow the score_<object> pattern (score_device, score_domain, score_ip, score_user), but lookup_phone and verify_email/verify_agent deviate by using different verbs. The naming is still readable and logically grouped, though a more consistent prefix like score_phone and score_email would improve uniformity.

Tool Count5/5

Seven tools cover the core signals for signup risk assessment without unnecessary redundancy. Each tool serves a clear purpose, and the count is well within the typical 3-15 range for a focused API.

Completeness5/5

The set covers the primary data points used in fraud/risk evaluation: phone, IP, email, domain, device, and a combined user score, plus a cryptographic agent verification for advanced scenarios. There are no obvious dead ends or missing lifecycle operations for this read-oriented risk assessment domain.

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