data_sources

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

With no annotations present, the description carries the full disclosure burden and meets it: it exposes the response shape, the semantics of `official`, a pipeline ordering property ('A keyed vendor is always asked before a scraped source'), and reconciles the tool as a statement of provenance rather than accuracy. This goes well beyond what any annotation would have provided.

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?

Four paragraphs each with a distinct job — what it is, how it differs from siblings along with the response shape, how to interpret `official`, and the accuracy caveat. The main claim is front-loaded in caps, and no sentence is filler; the length is proportionate to the behavioral disclosure it must make with no annotations or output 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?

For a zero-parameter metadata tool with no output schema, the description covers the response shape and explains the one semantically loaded flag (`official`) in depth, including consequences and how to cite it. Minor gaps remain: the sub-fields `label`, `connected`, and `serves` are named but their exact meanings are left to inference, and edge-case behavior (e.g., no sources) is not addressed.

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 tool takes zero parameters, so the input schema is trivially complete (100% coverage) and there is nothing to document; the baseline for 0-param tools is 4. The description instead spends its words on the return contract, which is the right place to focus.

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?

Opens with the tool's sole job — 'WHERE THIS READER'S MARKET DATA COMES FROM, and which of those sources are SCRAPED rather than licensed' — a concrete resource plus a discriminator. It then states how it differs from siblings ('Every other tool here names the vendor that answered it... This resolves those names'), so an agent cannot confuse it with the 51 data-returning tools around it.

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 establishes the tool's role by contrast with siblings: other tools emit vendor names, this one 'resolves those names.' It also gives actionable instructions on how to apply the output — treat `official` FALSE as weaker evidence, say so when resting a conclusion on one, and infer that a scraped figure means no connected vendor carried that surface. It never states an explicit invocation condition or exclusion, so the guidance is contextual rather than formal.

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