check_spam_text

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

With no annotations, the description carries the full burden. It discloses the use of GPT, Claude, and Gemini, vector similarity, and the output fields (verdict, confidence, category, risk signals, red flags, recommendation). Missing are potential limitations or failure modes, but the core behavior is well described.

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 three short sentences, each earning its place: the classification action, the return values, and the data provenance. There is no unnecessary detail or repetition.

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 tool's moderate complexity and the absence of an output schema, the description compensates by listing the return fields. It covers purpose, method, and outputs but omits edge cases like error handling or accuracy caveats, which are minor for this use case.

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 thorough descriptions for both 'text' (max 5000 chars) and 'locale' (ISO 639-1, auto-detected). The tool description adds no extra parameter-specific meaning, so the schema does the heavy lifting, warranting the baseline score.

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 starts with 'Classify a text message as SPAM, SUSPICIOUS, or SAFE' – a specific verb, resource, and outcome. The mention of multi-LLM consensus and vector similarity against scam patterns further differentiates it from sibling tools like get_spam_stats and get_spam_examples.

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 clearly implies the tool is for classifying text messages, distinguishing it from sibling tools that provide stats or examples. However, it does not explicitly state when not to use this tool or directly reference alternatives, so it falls short of a 5.

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