probe_widget_data

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It excels by documenting the return structure, explaining the meaning of each data_source value ('live', 'sample', 'error'), and noting that fields list only bindable dot-paths with sample values and that an empty payload has null fields. This goes well beyond basic transparency and prevents common misuse.

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, dense paragraph, but it is well-organized with a colon introducing the return shape and subsequent clauses explaining edge cases. Information density is high, and there is no filler, though breaking it into multiple sentences would improve readability. It earns a 4 because it is efficient and purposeful.

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 absence of an output schema and annotations, the description thoughtfully explains the return value and behavioral nuances (live/sample/error, field paths, null handling). It covers most of what an agent needs to safely invoke the tool. The missing 'options' parameter explanation is a notable gap, preventing a score of 5.

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 0% description coverage, so the description must compensate for parameter semantics. The 'widget' parameter is only implied as the target entity, and the 'options' parameter is completely undiscussed—its meaning, allowed values, and impact on the result are left unknown. The description focuses entirely on the output, leaving users to guess how to properly specify the 'options' object.

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 'Return a widget's data as JSON to pick field paths before binding a data primitive,' which clearly states the action, resource, and purpose. It is specific enough to distinguish this from siblings like list_widgets (which just lists widgets) or get_widget_options (which fetches configuration), making the tool's unique role clear.

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 frames the tool's usage: 'to pick field paths before binding a data primitive.' It also warns that the 'data_source' field distinguishes live data from sample/error, so users won't mistake placeholders for real results. While it doesn't explicitly name alternative tools or say 'don't use for X', the context is clear and actionable.

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