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onchain-forensics

vet_approach

Evaluates inbound opportunities by analyzing requests, links, and sender intentions to detect deceptive approaches. Never returns safe.

Instructions

Judge an inbound opportunity — podcast, interview, partnership, job, AMA — by what it ASKS, not by how good it looks. Built from a lure that worked on someone who verifies counterparties professionally: a 35-question production dossier citing his real work, using his own catchphrase, quoting his posts, and asking genuinely hard questions, because a flatterer never includes criticism and including it is what makes an approach read as journalism. The mechanism is effort as a trust signal: that much detail used to cost hours of human work, so nobody spent it on one target. That arithmetic no longer holds. So this does NOT score how convincing an approach is — that would give a forgery a good grade. It grades where a link actually points (a brand to the left of the registrable domain is a free label: wechat.web09eu.com is web09eu.com) and what the sender wants from you. Never returns safe.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linksNo
urgencyNo
platformNo
asksToInstallNo
asksForKeyOrSeedNo
asksForSignatureNo
asksForUpfrontPaymentNo
Behavior2/5

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

No annotations are provided, so the description must disclose behaviors. It mentions it grades links and sender wants, and does not score persuasiveness, but fails to explain output format, side effects, or limitations like what 'Never returns safe' means.

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

Conciseness2/5

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

The description is overly verbose and reads like a story, burying key functional details. It is not concise and wastes sentences on narrative rather than clear specification.

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

Completeness1/5

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

Given 7 parameters, no output schema, and no annotations, the description is severely incomplete. It does not adequately describe inputs, outputs, or behavior for an AI agent to use the tool correctly.

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

Parameters2/5

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

Schema description coverage is 0%, and the description only loosely hints at parameters like 'links' and 'asksToInstall'. It does not explain individual parameters, their types, or how they influence behavior.

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

Purpose3/5

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

The description states it judges inbound opportunities by what they ask, and grades links and sender wants, but the purpose is buried in a lengthy narrative. It is vague and not immediately clear what the tool does.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus its siblings. The description only says 'Never returns safe' but provides no context for appropriate usage or alternatives.

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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