AI-powered scam detection for suspicious texts, emails, and screenshots. US-focused.
Scored across 1 tool
Only one tool exists, so there is no risk of confusing it with other tools. The tool's purpose is clearly defined.
With a single tool named 'check', the naming is perfectly consistent by default. The verb form is clear and indicative of the action.
A single tool for a focused scam-checking service is reasonable, though slightly thin. The tool handles multiple input types, which justifies a single entry point.
The tool provides a complete verdict, summary, next steps, and confidence level. For a scam-checking service, no obvious operations are missing.
Check whether a message, URL, email, or piece of text is a scam. Returns a verdict (safe/scam/action/ignore), a plain-English summary, recommended next steps, and a confidence level.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | The message text, URL, or email content to analyze. Max 5000 characters. | |
| input_type | No | Whether the content is plain text or a URL. Defaults to 'text'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits itself. It explains the return values (verdict, summary, steps, confidence) but does not mention whether the tool modifies data, requires authentication, or handles errors (e.g., content exceeding 5000 characters). The read-only nature is implied but not explicit.
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 two sentences, no wasted words. It front-loads the core purpose and efficiently lists outputs. Every sentence adds value.
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 no output schema, the description adequately explains return values. However, it lacks notes on error conditions, rate limits, or authentication requirements. For a simple check tool, this is mostly complete but could be slightly more thorough.
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?
Schema coverage is 100% with clear parameter descriptions. The tool description adds minimal extra meaning (e.g., mentioning 'URL' or 'email' as input types) beyond what the schema already provides. The baseline of 3 is appropriate since the schema carries the semantic load.
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 clearly states the tool's function: checking if a message, URL, email, or text is a scam. It lists specific outputs (verdict, summary, steps, confidence), making the purpose unmistakable. Since no sibling tools exist, differentiation is not required.
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?
The description implies usage when evaluating scam content but lacks explicit guidance on when to avoid using it or compare with alternatives. No siblings are provided, so context is limited. A user can infer appropriate scenarios, but the description does not proactively clarify boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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