x_sentiment

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

No annotations are provided, so the description carries the full burden. It mentions the model (Workers AI Llama 3.1) but does not disclose whether the tool is read-only, authentication needs, rate limits, or what the output looks like. For an analysis tool, this is a significant gap.

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 a single, focused sentence that front-loads the core action and resource. Every word contributes to the purpose, making it highly concise with no wasted information.

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 and no annotations, the description is incomplete. It omits the return format (e.g., sentiment label, score, or narrative) and does not provide behavioral context like error handling or timeouts. An agent would be uncertain about what the tool returns after invocation.

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 covers 100% of parameters with a clear description ('Topic, ticker, or text sample to analyze'). The description adds context ('market & social') and 'tokens' but does not materially change the meaning beyond the schema, so the baseline score of 3 applies.

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 action ('Analyze') and the resource ('market & social sentiment for topics/tokens'), distinguishing it from sibling tools like summarize_text or embed_text. The mention of 'Workers AI Llama 3.1' adds specificity without confusion.

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 implied through the purpose (sentiment analysis), but the description does not explicitly state when to use this tool over alternatives or provide exclusions. Sibling tools are mostly unrelated, so no direct comparison is offered.

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