Email Fraud Detection

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

Annotations already provide readOnlyHint=true and idempotentHint=true, and the description adds behavioral context by listing what it checks (honeypot traps, spam traps, leaked credentials) and returns (fraud score, SMTP verification, domain age). There is no contradiction with annotations, and the description enriches the agent's understanding of the tool's behavior.

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 concise, with two sentences that front-load the key verb and resource. Every sentence adds value: the first lists checks, the second lists return values. There is no redundant or superfluous text, making it efficient for an agent to parse.

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 there is no output schema, the description compensates by listing return values (fraud score, SMTP verification, etc.). It covers the tool's purpose and outputs well. However, it could mention that the tool is read-only (already in annotations) or provide a hint about typical use cases, but overall it is fairly complete for a 3-parameter 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 input schema already provides 100% coverage with clear descriptions for all three parameters (email, fast, abuse_strictness). The tool description does not add any additional meaning or context for these parameters, so it merely meets the baseline without enhancing parameter understanding.

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 purpose with a specific verb ('Validate email for fraud risk') and lists concrete checks (deliverability, disposable providers, spam traps, etc.). The name and content differentiate it from sibling tools like ip_check, phone_check, and url_check, making its scope unambiguous.

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 for email fraud detection and mentions it 'goes beyond basic validation,' suggesting it's for in-depth analysis. While it doesn't explicitly state when not to use or name alternatives, the sibling tools are distinctly different (IP, phone, URL), so the context is clear enough for an AI agent to infer proper usage.

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