databazaar

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

Annotations declare readOnlyHint=true, so safety is covered. The description adds useful behavioral context: checkout_url enables one-click purchase and human_pitch aids operator messaging, which is not obvious from the name alone. However, it doesn't state permissions needed, whether the dataset must exist, or error behavior.

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?

Two concise sentences. The first enumerates the returned metadata fields, front-loading the primary value. The second adds distinguishing attributes (checkout_url, human_pitch). No waste.

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?

No output schema exists, so the description carries the burden of explaining returns and does so thoroughly (schema, counts, score, pricing, pitch, URL). An agent has enough to call correctly. Slight gap remains on when-not-to-use vs siblings, but the core is complete.

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 coverage is 100% with only one parameter (dataset_id) fully described. The description adds no syntax or format hints beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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?

States a specific verb (Get) and resource (dataset metadata) and enumerates exactly what the response contains: schema, column counts, quality score, price anchoring, checkout_url, human_pitch. An agent knows precisely what this returns, distinguishing it from preview_sample (data rows) and search_datasets (discovery).

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 usage by mentioning a dataset_id, but doesn't state when to use this tool vs preview_sample or search_datasets. No exclusions or alternative selection guidance is provided. A reasonable agent can infer it's a detail-fetch, but the description doesn't make it explicit.

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

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

Annotations only declare readOnlyHint=true and a title, so the description carries most of the behavioral load and does so well: it discloses the exact return payload (signed download URL, ~50 rows, schema), the optional synthesized-answer behavior, and that no purchase is required. It stops short of covering auth/permission needs or URL expiry.

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?

Three tight sentences: purpose first, then return shape, then the optional question behavior with a concrete example. No filler, and the most decision-relevant information is front-loaded.

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?

With no output schema, the description steps in to describe the return shape well (download URL, row count, schema, synthesized_answer field). For a simple two-parameter read tool this is nearly sufficient; only minor operational details like URL lifetime or size limits are absent.

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 both parameters are already fully documented in the schema, establishing the baseline of 3. The description reinforces the optional 'question' parameter and its synthesized_answer outcome, but adds no syntax or format detail beyond what the schema already provides.

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?

States a specific verb and resource (preview sample rows of a dataset) plus the concrete return (signed download URL for ~50 rows and the schema). It implies a distinction from full dataset access via 'no purchase required,' but never names the siblings get_dataset or search_datasets, so an agent must infer the boundary itself.

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?

Gives a clear purpose-driven context — use it to verify columns, format, and quality — which implies the when-to-use case. However, it never states when to prefer get_dataset or search_datasets instead, nor any prerequisites or exclusions, leaving the routing decision to inference.

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

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

Annotations already carry readOnlyHint=true, so the safety profile is covered. The description adds meaningful behavior beyond that: it discloses the return shape (ranked listings with prices, quality scores, checkout URLs) and the empty-result path. Rate limits and auth requirements are not mentioned, but the safety-relevant context is adequate.

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?

Three tight sentences, front-loaded with the core purpose and filters, followed by return shape and the empty-result fallback. Every sentence earns its place with no redundancy.

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?

With no output schema, the description compensates by describing the return values (ranked listings, prices, quality scores, checkout URLs) and the empty-result behavior. It is nearly complete for a 6-param search tool, missing only pagination/limit semantics now that limit is not referenced.

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 67%, and the description conceptually groups the filters (category, price, quality score, pricing type) but adds no syntax, format, or edge-case detail beyond the schema. With the schema doing most of the heavy lifting and one filter (limit) unmentioned, this is a baseline-adequate 3.

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?

Names a specific verb (search) and resource (datasets) within a named marketplace (DataBazaar), and enumerates the filter dimensions, so the tool's job is unambiguous. However, it does not explicitly distinguish itself from siblings get_dataset and preview_sample, leaving the agent to infer that this is the discovery step.

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?

Gives clear context for the tool's role (natural-language discovery over the marketplace) and adds a useful fallback: when results are empty, log_data_gap can signal demand. It stops short of explicitly stating when to prefer this over get_dataset or preview_sample, so no full when-not guidance.

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