Finds customers whose job feedback contains churn-risk language or a low satisfaction rating, and returns the exact sentence that triggered each written-feedback flag (or the rating evidence when no sentence matches).
| Name | Required | Description | Default |
|---|---|---|---|
| min_rating | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description usefully adds the result-selection rule (exact triggering sentence, falling back to rating evidence), but says nothing about result volume, pagination, or how min_rating gates the two detection paths.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler, and the output behavior is stated up front. Slightly dense with the parenthetical, but every clause carries information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema the description responsibly explains the return shape, and annotations cover safety. However, an agent still cannot confidently supply min_rating or know how this differs from churn_risk, leaving real gaps for a one-required-parameter detection tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter min_rating has 0% schema description coverage and the description never explains it: no scale or units (stars vs. percent), no direction (minimum rating to include vs. cutoff below which a flag fires), and no indication of whether it also gates the written-feedback sentence matching.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (finds) and resource (customers with churn-risk feedback) plus the precise return payload (triggering sentence or rating evidence). An agent can tell what it produces, but the description never distinguishes it from the sibling churn_risk tool, which sounds like overlapping territory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use or when-not-to-use guidance is given. With both 'churn_risk' and 'segment_customers' in the sibling list, the agent has no signal for choosing this tool over them; usage is only inferable from the name and the result description.
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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