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Check copy

check_copy
Read-onlyIdempotent

Check marketing or outreach copy (an email, a landing-page draft, a pitch, a social post) for the tells that make it read as AI-generated: cliché vocabulary (delve, unlock, empower, seamlessly integrate, etc.), negative-parallel framing ("not just X, but Y"), hedge words, heavy em-dash use, and diagnosing a problem with no fix in the same breath. No page or markup needed, just the text. Call this before sending copy to a human.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe raw copy to check

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds real behavioral detail beyond that: the exact detection criteria (cliché vocabulary, negative-parallel framing, hedges, heavy em-dash use, problem-without-fix) and the input constraint that no page or markup is needed. It does not describe what the check returns, which is its main remaining gap.

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

Conciseness5/5

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

Purpose and detection criteria come first, input guidance second, and the call-to-action last. Three sentences, each earning its place, with no filler or restatement of the tool name.

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

Completeness3/5

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

There is no output schema, and the description never says what the check produces — a verdict, a score, a list of flagged phrases, or rewritten copy. For a single-parameter analysis tool that is otherwise well specified, this is the one thing an agent cannot infer and would want before invoking it.

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?

Schema description coverage is 100% (one param, 'The raw copy to check'), so the baseline is 3. The description adds meaning on top by specifying 'No page or markup needed, just the text', clarifying that raw copy — not HTML or a URL — is expected.

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?

States a specific verb and resource ('Check marketing or outreach copy') and enumerates concrete input forms (email, landing-page draft, pitch, social post), so the agent knows exactly what is being analyzed. The scope ('tells that make it read as AI-generated') cleanly separates it from the sibling check_code and check_design tools, which operate on other artifacts.

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?

Gives an explicit trigger: 'Call this before sending copy to a human.' That is clear usage context. It does not state when *not* to use it or explicitly contrast with the siblings, but the input examples make the boundary obvious.

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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