ai_sentiment

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

With no annotations at all, the description carries the full burden. It does disclose the method (lexicon-based, implying deterministic rather than ML scoring) and the output range/label, which is meaningful context, but says nothing about accuracy limits, language detection failure, text length limits, or determinism guarantees.

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?

Two short sentences, front-loaded with the core capability and method, with the return contract second. Nothing is wasted or buried.

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?

An output schema exists, so return values needn't be re-explained, and the description sensibly summarizes them anyway. However, for a tool with zero annotation coverage and an undocumented parameter, it omits behavioral edge cases (language fallback, empty/very long text) that an agent would need before calling it confidently.

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?

There is one parameter with 0% schema description coverage, so the schema adds no meaning. The description implies the parameter is the text to score but never states length limits, encoding, or how language is determined, leaving the sole parameter under-specified.

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 names a specific operation and resource ('lexicon-based sentiment ... text') and constrains the input domain to Spanish/English. It is clearly distinguishable from siblings like ai_text_stats or ai_keywords, though it never explicitly contrasts itself with them.

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?

Usage is only implied: an agent can infer this is the tool for sentiment scoring, but there is no statement of when to prefer it, no exclusions, and no mention of what happens with other languages or mixed-language input.

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