get_sentiment

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

With no annotations provided, the description carries the full burden. It discloses behavioral traits like returning sentiment score (-1 to 1), actionable recommendation, and AI confidence level, which helps understand output. However, it lacks details on rate limits, error handling, or performance characteristics that would be useful for an agent.

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 appropriately sized and front-loaded, starting with the core functionality. Each sentence adds value, such as explaining the return values and use cases, with no redundant or 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 the tool's complexity (sentiment analysis with AI) and no output schema, the description does a good job explaining return values (sentiment score, recommendation, confidence). However, it could be more complete by detailing error cases or limitations, especially since annotations are absent.

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 100%, so the schema already documents the market_id parameter. The description does not add any specific meaning or usage details about the parameter beyond what the schema provides, such as examples or constraints, resulting in a baseline score.

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 performs 'AI-powered sentiment analysis, recommendation, and confidence score for any prediction market,' specifying both the action (get analysis) and resource (prediction market). It distinguishes from siblings like get_probability by emphasizing it 'goes beyond raw probability' to analyze psychology and crowd wisdom.

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 clear context for when to use it: 'for trade signals, contrarian analysis, or augmenting your own prediction models with market sentiment data.' However, it does not explicitly state when not to use it or name alternatives among siblings, such as get_probability for raw probability data.

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