x_sentiment

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It states the tool analyzes sentiment but does not describe what the output looks like, whether it has side effects, or any rate limits/authentication requirements. This is a minimal level of transparency, similar to the 'update_drive' example.

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 sentence with no redundant words. It front-loads the core action ('Analyze') and resource ('market & social sentiment') and adds the model for extra context. Every word earns its place.

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 simplicity (one parameter, no output schema), the description is passable but has a clear gap: it never explains the return value or format. The agent is left unsure whether the result is a score, classification, or text. With no output schema and no annotations, the description should at least mention what the analysis yields.

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 full coverage of the only parameter 'topic' with a clear description ('Topic, ticker, or text sample to analyze'). The description adds context about sentiment and the model, but it does not elaborate further on the parameter's semantics or acceptable formats. This meets the baseline of 3 for high schema coverage.

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 uses a specific verb 'Analyze' with a clear resource ('market & social sentiment for topics/tokens') and even identifies the model ('Workers AI Llama 3.1'). This makes the tool's purpose immediately clear and distinct from its siblings, which are focused on other domains like domain checking, price summaries, or text embedding.

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 no guidance on when to use this tool versus alternatives. There is no mention of exclusions, prerequisites, or comparison to siblings like summarize_text or dex_price_summary. The only implied usage is from the tool name and the verb 'analyze', but no explicit context or conditional instructions are given.

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