redreplier

Explain Mention Score

explain_mention

Generates and stores AI relevance reasoning, tags, and a drafted reply for a single mention, returning the full object or null if unknown. Use for one-off analysis or before updating status.

Instructions

Get the AI relevance reasoning (relevanceReason), tags, and a drafted reply (aiReplySuggestion) for one mention, generating whatever is missing on first call and storing it, so later calls are instant reads. The website must have a description; without one the mention comes back unchanged. Use it when a score looks wrong or before update_mention_status on a borderline lead, not across every row of list_mentions, since generation is slow. Returns the full mention object, or null (not a 404) when the ID is unknown to this account.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mentionId`	Yes	Mention ID (UUID) from list_mentions

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	No	The full mention with relevanceReason, tags, and aiReplySuggestion filled in; null when the mention is not found.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.1.1Sep 7, 2026

changedInput schema / properties / mentionId / description

Previous value: -"Mention ID (UUID)"New value: +"Mention ID (UUID) from list_mentions"

changedOutput schema / properties / result / description

Previous value: -"The AI relevance reasoning and tags for the mention."New value: +"The full mention with relevanceReason, tags, and aiReplySuggestion filled in; null when the mention is not found."

First observedv0.1.0Sep 7, 2026

TDQS

A4.6/5.0

Behavior5/5

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

The description discloses that the tool generates and stores missing data on first call, making later calls instant reads—a significant behavior not captured by annotations. It also clarifies the return behavior for unknown IDs (null, not 404). These details go well beyond the annotation hints and provide essential operational context.

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 concise and front-loaded with the core action, followed by behavior, usage guidance, and return semantics. Each sentence serves a purpose, and the length is appropriate for the complexity. It could be slightly trimmed but is efficient.

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?

Given the tool's complexity (one parameter, generation behavior, storage, slow performance, prerequisites), the description covers all necessary aspects: when to use, what to expect, return values, and the website description prerequisite. The output schema exists (though not shown), so return details are covered. The description is complete for an agent to decide and invoke correctly.

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?

The single parameter mentionId is fully described in the input schema (UUID from list_mentions), and the description does not add extra parameter-level semantics beyond what's already provided. Since schema coverage is 100%, the baseline of 3 is appropriate; the description adds no new parameter details, but it does not need to.

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's purpose: retrieving AI relevance reasoning, tags, and a drafted reply for a single mention. It also distinguishes itself from siblings by explicitly mentioning update_mention_status and list_mentions, making the resource and action unambiguous.

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?

It explicitly provides when to use ('when a score looks wrong or before update_mention_status on a borderline lead') and when not to use ('not across every row of list_mentions, since generation is slow'). It also mentions a prerequisite (website must have a description) and the consequence if missing. This is comprehensive guidance.

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