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Rescan AI mentions (Pro+)

run_ai_mentions_scan

Forces a fresh AI-mentions scan now instead of waiting for the weekly job. Pro+ only. Spends a DataForSEO scan, so one scan per site per hour (a 429 includes nextRefreshAvailable). Blocks until the scan is saved (usually under a minute) and returns the new snapshot summary.

Input Schema

NameRequiredDescriptionDefault
llmNoScan one model only (default all).
siteYesThe site to act on. One of: fencestainingdenver.com.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations only flag a non-read, non-idempotent write; the description goes well beyond that by disclosing the paid resource cost (a DataForSEO scan), the rate-limit in prose and the 429 error shape with nextRefreshAvailable, the blocking/latency behavior (usually under a minute), and the Pro+ entitlement requirement. This is exactly the operational context annotations cannot carry.

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?

Four short sentences, each earning its place, with the core action and the 'now vs weekly' distinction front-loaded before cost, limits, and blocking behavior. No repetition of the title or schema.

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?

With no output schema, the description still characterizes the return ('the new snapshot summary') and covers cost, quota, error signaling, latency, and entitlement. Nothing material for correct invocation is missing.

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 100%, so both parameters are already documented (including the llm enum and the site value). The description adds no parameter-level detail beyond the schema, so the baseline of 3 applies.

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 names a specific action (force a fresh AI-mentions scan now) against a named alternative behavior (the weekly job), making it clearly distinct from the read-side sibling get_ai_mentions. An agent can identify the resource and the effect without opening the schema.

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?

It states when to use this over the default path ('now instead of waiting for the weekly job') and gives the gating conditions (Pro+ only, one scan per site per hour). It stops short of explicitly naming get_ai_mentions as the read alternative, so routing is inferred rather than spelled out.

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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