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

Without annotations, the description carries the burden of behavioral disclosure. It explains that the response includes access status per output port and inlines data contracts, giving useful insight into what the tool returns. However, it does not mention error handling, permissions, or rate limits, leaving some behavioral aspects unspecified.

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 concise and front-loaded with the main purpose, followed by Args and Returns sections. It is well-structured without filler, though the Returns section might be considered redundant given the existence of an 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 simple get-by-ID tool with an output schema, the description covers the core functionality and return contents. However, it lacks explicit usage guidance relative to sibling tools and does not address error cases or edge conditions, making it minimally 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?

The only parameter, data_product_id, is described as 'The data product ID,' which merely restates the schema's property title. With 0% schema description coverage, the description was expected to compensate with additional meaning (e.g., format, usage, examples), but it fails to provide any beyond the obvious.

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 clearly states 'Get a data product by its ID,' using the verb 'Get' and identifying the specific resource (data product) and key (ID). It distinguishes from sibling tools like dataproduct_search (search vs. get) and dataproduct_query (query vs. get).

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?

Usage is implied: the description says 'by its ID', indicating it is for direct retrieval by ID. However, it does not explicitly state when to use this tool instead of alternatives (e.g., searching by name), nor does it mention exclusions or prerequisites.

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?

With no annotations provided, the description carries the full burden. It transparently discloses that the tool connects to Snowflake/Databricks, executes the provided SQL, enforces data contract terms, and returns at most 100 rows. It does not explicitly clarify whether only read-only queries are allowed or describe error behavior, which prevents a perfect score.

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 concise and well-structured: a clear opening sentence, necessary governance/access context, and a straightforward Args/Returns layout. No filler or 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?

The description covers access requirements, data contract compliance, parameter meanings, and return shape (row count and 100-row limit). Since an output schema exists, return details need not be fully repeated. It lacks explicit error conditions and read-only clarification, so it is strong but not exhaustive.

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, but the Args section fully defines every parameter, including the business-purpose requirement and the tip to use fully qualified table names. This adds substantial meaning beyond the raw 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 clearly states the action ('Execute an SQL query') and the target ('a data product's output port'). It also distinguishes itself from siblings like search/get/request_access by specifying that the tool runs SQL against the underlying data platform.

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?

It states prerequisites: access to the output port is required, and queries must comply with data contract terms. It does not explicitly name sibling tools as alternatives or specify when not to use the tool, so it stops short of a 5.

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?

With no annotations, the description carries the full burden. It discloses that the tool creates an access request, that access may be auto-granted or reviewed by the owner, and that the response includes access details. This goes beyond simple 'requests access' without contradicting any annotations.

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 concise and well-structured: a one-line summary, a brief process note, a clean Args list, and a Returns line. Every sentence adds value without unnecessary verbosity.

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?

The description covers the tool's purpose, process, all parameters, and return structure. With an output schema present and no nested objects, this is sufficient for an agent to select and invoke the tool correctly. It could possibly mention prerequisites or failure modes, but none are critical for this action.

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's Args section is essential. It explains all three parameters in plain terms and adds extra guidance for the purpose parameter ('Use a high-level description of why you need this data.'), fully compensating for the schema gap.

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 uses a specific verb 'Request access' with a clear resource ('output port of a data product') and adds context about the access review process. It is clearly distinguishable from sibling read-only tools like search, get, and query.

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 states this tool is for requesting access to a data product output port, which makes the use case obvious. It does not explicitly mention alternatives or when-not-to-use, but the context alone is strong enough to guide an agent.

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?

With no annotations, the description carries the burden. It discloses that only active data products are returned and that the result is a list of summaries, which is useful. However, it omits details like authentication requirements, error behavior, pagination, or what 'basic information' includes, leaving some ambiguity.

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 very concise, with a clear one-sentence summary followed by structured Args and Returns sections. Every sentence provides value, and the format aids quick comprehension.

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 simple single-parameter search tool, the description is mostly complete. It states the purpose, the active-only filter, and the return type. The existence of an output schema likely handles detailed return formats, but the description could note whether pagination or large result sets are handled. Overall, adequate for typical usage.

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 schema provides only type and default for search_term, with no description. The description fills this gap effectively by explaining the parameter's purpose and supporting multiple space-separated terms. This adds meaningful context beyond 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 clearly states the action (search), the resource (data products), and the scope (active only). It naturally distinguishes from siblings like dataproduct_get or dataproduct_query by focusing on search functionality.

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 when to use it (for searching data products) and notes the active-only restriction, but it does not explicitly compare with alternatives or state when not to use it. Sibling tools are not mentioned, so guidance is limited to the basic scenario.

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