get_token_sentiment

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the return values (sentiment score, social volume, trending topics) and includes a price ('Price: $0.25'), which adds some context about cost. However, it lacks critical details such as rate limits, authentication requirements, data freshness, or error handling, leaving significant gaps for a tool with no annotation coverage.

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 appropriately sized: it starts with a clear purpose statement, lists parameters with brief semantics, specifies returns, and includes pricing. Each sentence adds value without redundancy. It could be slightly more front-loaded by integrating the price into the main flow, but overall it's efficient and easy to parse.

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 that there is an output schema (implied by 'Has output schema: true'), the description doesn't need to fully explain return values, which it partially does. However, with no annotations and 0% schema coverage, the description should provide more behavioral context (e.g., rate limits, errors) to be fully complete. It covers basics like purpose and parameters but misses advanced usage details.

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?

Schema description coverage is 0%, so the description must compensate. It lists parameters in an 'Args' section with brief explanations (e.g., 'token_address: The token contract address (0x...)'), adding meaning beyond the bare schema. However, it doesn't fully detail parameter constraints (e.g., valid chain values beyond the default 'ethereum'), leaving some ambiguity. With two parameters and partial compensation, a baseline score is appropriate.

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's purpose: 'Get AI-powered social sentiment analysis for a token.' It specifies the action ('Get AI-powered social sentiment analysis') and resource ('for a token'), which is clear and specific. However, it doesn't explicitly differentiate from sibling tools like 'get_sentiment_analysis' or 'get_stock_sentiment', which reduces it from a perfect score.

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. It doesn't mention sibling tools like 'get_sentiment_analysis' or 'get_stock_sentiment', nor does it specify contexts or exclusions for usage. The only implicit hint is the focus on tokens, but this is insufficient for effective tool selection.

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