get_bitcoin_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 discloses that the tool returns detailed psychological metrics in string format, including community sentiment and Fear & Greed Index history. However, it does not address data freshness, rate limits, or any potential failure modes.

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 with bullet points and use cases. It is appropriately sized, front-loads the main purpose, and every sentence adds value. No wasted words.

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 low complexity (no parameters, has output schema), the description is complete. It explains both elements of the response (community sentiment and Fear & Greed Index history) and provides use cases. The output schema exists, so return value explanation is optional but here it adds 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?

The input schema has zero parameters, and the description does not need to explain parameters. According to guidelines, 0 params gives a baseline of 4. The description adds rich detail about the output, which is valuable.

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 it provides 'comprehensive sentiment analysis' based on community voting and Fear & Greed Index. This distinguishes it from siblings like get_bitcoin_market_data and get_bitcoin_price_usd, which focus on price and market data rather than 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 explicitly lists use cases: 'when you need to gauge overall market sentiment, understand investor psychology, or determine if the market is in fear or greed mode.' It does not mention when not to use or alternative tools, but the context is clear.

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