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

With no annotations, the description carries the full behavioral burden and adds useful context: it names the source (Polymarket), states the return format (0-100 score with bullish/bearish label), and discloses a $0.01 USDC cost per call. However, it omits payment prerequisites, rate limits, and any failure behavior, so transparency is adequate but incomplete.

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?

Three tight sentences front-load the core action, then the source and output, then the cost. Every sentence adds relevant information with no waste.

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 two-parameter tool with no output schema, the description explains what is returned (0-100 sentiment score with label) and discloses cost. It is nearly complete, though it could mention aggregation method or time window for full contextual clarity.

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 both the topic and limit parameters are already documented in the input schema. The description does not add syntax, constraints, or interpretation beyond what the schema provides, making the baseline 3 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 uses a specific verb and resource: 'Aggregate sentiment across all prediction markets related to a topic.' It clearly distinguishes from raw odds/event siblings by emphasizing an aggregated 0-100 sentiment score, though it does not explicitly name alternatives like prediction_market_aggregator or crypto_sentiment.

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 implies usage for topic-based sentiment analysis but gives no explicit when-to-use or when-not-to-use guidance. It does not contrast with sibling tools such as prediction_market_aggregator, prediction_market_odds, or crypto_sentiment, leaving the agent to infer selection criteria.

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