get_sentiment_analysis

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

No annotations are provided, so the description carries full burden. It mentions 'Price: $0.30' which is useful cost context, but lacks other behavioral details like rate limits, authentication needs, data freshness, or error handling. The description doesn't contradict annotations (none exist), but it's insufficient for a mutation-like tool (though sentiment analysis is likely read-only).

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 clear sections (Args, Returns, Price) and uses bullet-like formatting. It's relatively concise at 5 lines, though the 'Price' line could be integrated more smoothly. Most sentences earn their place by adding value beyond the tool name.

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 no annotations, 0% schema coverage, but an output schema exists (implied by 'Has output schema: true'), the description is moderately complete. It covers basic purpose, parameters, returns, and cost, but lacks behavioral context and usage guidance. The output schema reduces need to explain return values, but more operational details would help.

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 schema provides no parameter descriptions. The description adds some semantics: it explains 'query' as 'Topic to analyze (token name, project, or keyword)' and 'sources' as 'List of sources. Options: twitter, reddit, news, telegram'. This clarifies purpose and options, but doesn't fully compensate for the coverage gap (e.g., no details on source combinations or query formatting).

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 sentiment analysis for crypto topics.' It specifies the action (get sentiment analysis) and resource (crypto topics). However, it doesn't explicitly differentiate from sibling tools like 'get_token_sentiment' or 'get_stock_sentiment' beyond mentioning 'crypto topics'.

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_token_sentiment' or 'get_stock_sentiment' that might be related, nor does it specify prerequisites, constraints, or typical use cases beyond the basic function.

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