Search AI Classification
search_ai_classificationSearch AI scans by classification.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| classification | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
search_ai_classificationSearch AI scans by classification.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| classification | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Input schema / properties / classification / enumAdded value: +[
+ "LEGITIMATE",
+ "LOW_RISK",
+ "MODERATE_RISK",
+ "HIGH_RISK",
+ "CONFIRMED_SCAM"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, and the description adds no behavioral context beyond that. It does not mention result limits, ordering, pagination, or any other operational trait, so it adds little value beyond the structured annotations.
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?
The description is a single short sentence with no filler and is easy to parse. It front-loads the action and the filter, making it appropriately concise for such a simple tool.
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?
Given the output schema and rich annotations, an agent can likely invoke the tool correctly with the enum value and optional limit. However, the description does not clarify how this differs from the related AI search siblings or what an 'AI scan' classification represents in context, leaving some selection ambiguity.
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?
With 0% schema description coverage, the description should compensate, and it does identify classification as the search criterion. However, it says nothing about the limit parameter or the meaning of the classification values beyond what the enum itself provides, so the added semantic value is minimal.
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?
The description states a clear verb ('Search'), a resource ('AI scans'), and a filter ('by classification'), so an agent can tell this is a filtered lookup. It does not distinguish itself from sibling tools like search_ai_high_risk or search_ai_scam_type, so it stops short of a 5.
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?
There is no guidance on when to use this tool versus the many sibling search tools. 'By classification' largely restates the tool's name and provides no context for choosing it over search_ai_high_risk or search_ai_scam_type.
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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