analyze_data_insights

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the return shape (JSON), default values (min_frequency=3, top_n=20), and prominently warns that date_range must be an object and not an integer. However, it does not state whether the tool is read-only, whether it makes external/network calls, any rate/auth constraints, or expected cost/latency — notable gaps for a zero-annotation tool.

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 uses a clear Args/Returns/Examples structure, front-loads the purpose, and every parameter earns its place given the complexity (5 params, 3 modes, conditional applicability). It is on the longer side, but the length is justified by the multi-mode surface; nothing is redundant. Minor trimming of the mode explanations could tighten it without loss.

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 and zero annotations, the description is very nearly complete for correct invocation: modes, parameter semantics, defaults, format constraints, and runnable examples are all covered, and an output schema exists so the sketchy Returns line is acceptable. The main gap is the absence of guidance on how this relates to the many analysis siblings, which leaves a routing decision to the agent.

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 fully compensate — and it does, thoroughly. It documents insight_type with three named options and their meanings, defines date_range's object format with example JSON and an explicit 'must not pass integer' warning, and explains the mode-scoped role of topic, min_frequency, and top_n including defaults. This is exemplary parameter documentation given the empty 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 states a clear unified purpose — a multi-mode data insight analysis tool — and enumerates three specific modes (platform_compare, platform_activity, keyword_cooccur) with one-line explanations for each. This gives a concrete verb+resource with meaningful subtypes. However, it never explicitly differentiates itself from the many analysis siblings (analyze_topic_trend, analyze_sentiment, compare_periods, aggregate_news), so an agent must infer how it differs from those.

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 internal usage guidance — which parameters apply to which mode (topic for platform_compare, min_frequency/top_n for keyword_cooccur) and three concrete examples of valid calls. This is step-by-step operating guidance. But it gives no guidance on when to choose this tool versus alternatives like analyze_topic_trend or analyze_sentiment, and with 26 siblings this relational routing is absent.

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