get_market_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 effectively discloses the semantic structure of returned data (0-100 scale, five classification tiers, historical data). While it doesn't mention caching, rate limits, or API dependencies, it provides sufficient behavioral context for an agent to interpret the results correctly.

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 consists of two efficient sentences. The first front-loads the core action and data structure; the second provides usage context. There is no redundant information or unnecessary verbosity—every word serves a specific purpose.

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 this is a zero-parameter tool with an output schema present, the description appropriately focuses on explaining the semantic meaning of the returned data (Fear & Greed classifications) rather than input mechanics. It fully covers what an agent needs to know to invoke and interpret results.

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 has zero parameters, which establishes a baseline score of 4 per the evaluation rules. The description correctly implies no filtering or configuration is needed by focusing entirely on the fixed data structure returned.

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 explicitly states it retrieves the 'Bitcoin Fear & Greed Index' and details the specific data returned: current value (0-100), classification categories, and 7-day history. This clearly distinguishes it from sibling tools like get_btc_price (raw price data) and analyze_block (technical blockchain analysis) by focusing specifically on sentiment metrics.

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 usage context: 'Use this to gauge market sentiment alongside price data.' This implies complementary use with tools like get_btc_price and clarifies the tool's domain (sentiment vs. technical analysis). It lacks explicit 'when not to use' exclusions, but effectively signals its purpose within the broader toolset.

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