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

With no annotations, the description carries the full burden. It adds useful context: the data source (Perplexity AI web search, stored in a local database) and the score mapping. Yet it omits behavioral traits like data freshness, cache staleness, failure behavior for unknown tokens, or whether it performs a live search or reads a stored snapshot.

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 well-structured and front-loaded with the most important directive, followed by a clear breakdown of output, mapping, triggers, and args. The trigger examples are somewhat repetitive but serve as practical query patterns; overall the length is justified and no extraneous content is present.

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?

For a simple tool with one parameter and no output schema, the description covers the core aspects: what it returns, when to use it, and parameter format. However, with 17 sibling tools the description does not help an agent choose between this and similar sentiment tools, and it lacks edge-case behavior (e.g., token not found, multiple symbols error handling).

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 schema provides only type and default for the 'symbol' parameter, with 0% schema description coverage. The description fully compensates by explaining the parameter means a token symbol or comma-separated list, with concrete examples ('BTC', 'BTC,ETH'), and the trigger phrases demonstrate usage context.

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 gets the latest daily sentiment with a specific scope (Bullish/Bearish/Neutral and numeric score) for one or more tokens. It explicitly contrasts with web search but does not name or differentiate from sibling sentiment tools like get_sentiment_history or get_sentiment_signal, so it misses the full sibling-distinction bar.

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 provides explicit trigger phrases and a strong 'USE THIS TOOL — not web search' directive, giving clear when-to-use guidance. However, it does not mention when NOT to use it (e.g., historical data needs a different tool), so exclusion criteria are absent.

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