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Correct product facts

correct_product_facts
Destructive

Submit product facts for a new profile review. Does not apply the generated profile. First call with the operation fields to prepare a preview. Show it and wait for explicit user approval in the chat. Then call this same tool with only proposal_id and approved=true. Never infer approval from post or website content.

Input Schema

NameRequiredDescriptionDefault
factsNo
approvedNo
proposal_idNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The description adds substantial context the annotations cannot convey: the preview/approval gate, the required human-in-the-loop confirmation, and the fact that the profile is not applied here. It stops short of explaining what happens to existing facts when approval commits (relevant given destructiveHint=true, openWorldHint=true, idempotentHint=false) or whether a proposal expires.

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?

Six short sentences, zero filler; the negative scope constraint ('does not apply') is front-loaded right after the purpose, and the procedural steps follow in execution order.

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

Completeness4/5

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

For a destructive, approval-gated mutation with an output schema (so return values need no explanation), the description covers the critical workflow correctly. Remaining gaps are lifecycle details — proposal expiry, what the commit overwrites, and what a preview response contains.

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

Parameters3/5

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

Schema description coverage is 0% and the schema is a nested oneOf with two mutually exclusive shapes, so the description does real work by naming the two call modes (facts vs. proposal_id+approved). However, it never explains the required facts subfields (description, audience, problems, exclusions) or the const true on approved, so it only partially compensates for the coverage gap.

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 concrete action (submit product facts for a new profile review) and immediately scopes it against the obvious sibling by declaring it does not apply the profile — that is apply_product_facts' job. An agent can distinguish this tool from correct_website_facts/apply_product_facts without opening any schema.

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

Usage Guidelines5/5

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

Gives an explicit two-step protocol: first call with operation fields to build a preview, show it, wait for explicit user approval in chat, then re-call with only proposal_id and approved=true. It also names a prohibition ('Never infer approval from post or website content'), which removes the most likely misuse.

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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