x_analyze

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context beyond that, explicitly stating 'No new network fetches or LLM inference; sample statistics only,' which tells the agent the tool is purely computational over saved data. This enriches the annotation information without contradicting it.

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 entire description is one focused, front-loaded sentence. The core action and resource come first, followed by the most important behavioral constraints. There is no filler or redundant restatement of the schema.

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 that the tool has an output schema and annotations covering read-only, idempotent, non-destructive behavior, the description supplies the remaining needed context: what is aggregated, the categories, and the no-network/no-inference constraint. It is complete enough for an agent to select and call the tool correctly, though it could be slightly stronger with explicit mode-by-mode semantics.

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 description must compensate. It partially does: 'saved public post results' clarifies what result_ids refer to, and the list 'activity, engagement, link domains, hashtags' maps directly to the mode enum values. However, it does not explain the maxItems constraint, the default mode, or what each specific mode returns, leaving the agent to infer those from the schema.

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 opens with a specific verb and resource: 'Aggregate saved public post results,' followed by the exact aggregation dimensions. The clause 'No new network fetches or LLM inference' further distinguishes it from sibling tools like x_search, which retrieve live data. This is unambiguous and easily separable from the other tools.

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 clearly scopes when the tool is appropriate: it operates only on already-saved public post results and performs no new network fetches or LLM inference. This is a useful exclusion that prevents an agent from choosing it for live search tasks, though it does not explicitly name alternatives or state conditions like 'use x_search when you need fresh posts.'

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