analyze_reviews

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose that pre-fetched reviews cause other filtering parameters to be ignored, and that filterOfftopicActivity defaults to false to show all reviews including controversies. However, it doesn't mention rate limits, API call behavior, or what happens when fetching fails. The description adds some behavioral context but not comprehensive.

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 a single, focused sentence that front-loads the core purpose and then lists key capabilities. It's concise and every phrase adds value. It could be slightly more structured (e.g., separating the core function from optional features), but it's efficient and readable.

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 (9 parameters, no output schema), the description covers the main capabilities and important behavioral notes (pre-fetched reviews, filtering behavior). It doesn't explain return value structure, but since there's no output schema, that's a gap. However, the description is reasonably complete for an agent to understand what the tool does and when to use it.

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 all 9 parameters. The description adds context about the pre-fetched reviews parameter ('Useful to avoid duplicate API calls') and clarifies that filtering parameters are ignored when pre-fetched reviews are provided. However, most parameter semantics are already in the schema, so the description adds marginal value beyond it.

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 function: 'Fetch and analyze Steam game reviews to extract sentiment, common themes, and key insights.' It uses a specific verb ('analyze') and resource ('Steam game reviews'), and distinguishes itself from sibling tools like fetch_reviews by combining fetching with analysis. The mention of optional topic drill-down, time-bounded analysis, and pre-fetched reviews further clarifies its scope.

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 when to use this tool: when you need sentiment, themes, and insights from reviews, rather than just raw reviews (fetch_reviews). It also mentions the pre-fetched reviews option to avoid duplicate API calls, which is a clear usage hint. However, it doesn't explicitly state when NOT to use it or name alternatives like fetch_reviews for raw data needs.

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