healthcare-data

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

With no annotations, the description carries the full burden of behavioral disclosure. It indicates the tool lists access options, but does not describe what the return value looks like, whether it requires a slug to identify the product, or any other behavioral traits. The lack of detail about the output or side effects leaves the agent guessing.

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, concise sentence with a clear structure: an overview ('Every way to get full access to a Dataplex product') followed by a colon and a list of examples. Every part earns its place, and it is immediately scannable.

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 tool is simple (one param, no output schema, no annotations), but the description is incomplete because it fails to explain the required 'slug' parameter and does not describe what the tool will return. While the title and examples give some context, the agent is missing critical information to invoke the tool correctly.

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 has one required parameter 'slug' with zero description coverage, and the tool description does not mention it at all. The description fails to compensate for the missing schema documentation, leaving the agent to infer that 'slug' likely identifies a product but without any explicit explanation.

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 tool's purpose with a specific verb ('get') and resource ('access options'), and enumerates categories (Snowflake Marketplace, Databricks, free trial, docs, human contact) that distinguish it from siblings like pricing or product details.

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 the tool is used when you need access options for a product, but it gives no explicit guidance on when to use it versus alternatives or any exclusions. There is no mention of when not to use it or how it relates to sibling tools like get_pricing or get_sample_data.

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?

No annotations are provided, so the description carries the full burden. It only lists the returned content (tables and SQL queries) and mentions 'validated' queries, but doesn't disclose side effects, access requirements, or error behavior. The safe read-only nature is implicit rather than explicit.

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 one concise sentence with no filler. However, it is a noun phrase fragment rather than a clear imperative sentence, which slightly reduces readability. It is front-loaded with the most important content but could be structured more directly.

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, no annotations, and an undocumented parameter, the description is insufficient for an agent to fully understand the tool. It doesn't explain what 'tables and validated example SQL queries' will actually be returned, how they relate to the product, or how this tool compares to similarly named siblings.

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 single required parameter 'slug' has a 0% schema description coverage and the description does not explain its meaning or format. While 'one Dataplex product' implies slug identifies a product, this is not concrete enough for an agent to know exactly what value to provide.

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 identifies the tool's output as 'Tables and validated example SQL queries for one Dataplex product', which clarifies content but lacks a clear verb like 'retrieves' or 'returns'. It does not explicitly state this is about a data dictionary, and overlaps with sibling tools like get_sample_data and get_snowflake_trial_sql.

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 phrase 'enough to plan an analysis before subscribing' implies use in a pre-subscription planning context, but no explicit alternatives or exclusions are given. It doesn't differentiate from sibling tools such as get_product_details or get_access_options.

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 transparency burden. It states the output is 'machine-readable' but does not disclose format details, whether it returns all products at once, or if any authentication is needed. For a read-only parameterless operation, the description provides basic transparency but lacks richer behavioral context.

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, well-structured sentence that front-loads the core purpose and then lists the specific data included. Every word contributes value with no redundancy or filler. It is concise while remaining informative.

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 parameterless tool with no output schema, the description is largely complete: it states the scope ('all Dataplex products') and enumerates the return fields. Minor gaps remain, such as the exact data format or whether prices are in a particular currency, but these are not critical for a simple pricing lookup. The description covers the essential context needed for an agent to select and invoke the tool.

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 has zero parameters, and the input schema is empty, so there are no parameter semantics to explain. The baseline for 0 params is 4, and the description adds relevant context about the output content, which is sufficient. No additional parameter documentation is needed.

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 specifies the tool's purpose: retrieving machine-readable pricing for Dataplex products. It enumerates the specific data returned (monthly price, free-trial link, marketplace URL), which distinguishes it from sibling tools like get_product_details or get_sample_data. The verb is implied but the resource and scope are explicit.

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 no guidance on when to use this tool versus alternatives such as get_product_details or list_products. There is no mention of prerequisites, exclusions, or preferred use cases. The only usage signal is the tool name and the description's focus on pricing, which is implicit rather than 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what is returned (metadata fields) and implies a read-only operation, but does not specify potential errors, permission requirements, or behavior for invalid slugs. For a simple retrieval tool this is adequate but not rich.

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, compact sentence that front-loads the key phrase 'Full metadata' and then lists concrete content fields. Every word earns its place, with 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?

For a tool with a single parameter and no output schema, the description provides a solid list of return fields, making the tool's output predictable. It does not explain how to construct the slug or handle errors, but those are minor gaps for this level of complexity.

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 one required parameter, 'slug', with no schema-level description. The tool description mentions 'one Dataplex product' but does not explicitly define what the slug parameter is or how it should be used. With 0% schema description coverage, the description should compensate more than it does.

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 tool's purpose: returning full metadata for a single Dataplex product, and enumerates the specific fields included (description, tables, update frequency, price, docs, Snowflake Marketplace listing). This distinguishes it from sibling tools like get_pricing or get_data_dictionary by emphasizing comprehensive metadata.

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 this tool—when you need complete metadata for one specific product—but does not explicitly mention alternatives or exclusions. Sibling tools like get_pricing or get_data_dictionary are not referenced, so an agent must infer the boundary from the listed fields.

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?

The description discloses useful behavioral details: no signup required and real data. However, it claims '100 REAL rows' while the max_rows parameter defaults to 25, creating a potential conflict with the schema. This inconsistency reduces clarity about actual tool 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?

The description is two sentences, front-loaded with the core functionality and value. Every word earns its place, with no superfluous content.

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 main purpose and a key behavioral trait (no signup needed), but it omits parameter definitions and the discrepancy with max_rows is unaddressed. For a simple tool, it is mostly adequate but not fully 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 description coverage is 0%, and the description provides no information about the 'slug' or 'max_rows' parameters. The agent must infer that 'slug' identifies the product, but this is not explicit. The description fails to compensate for the lack of schema documentation.

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 tool's function: returning 100 real rows from a Dataplex product's primary table. It also specifies the intended use ('evaluate schema and content quality'), distinguishing it from siblings like get_data_dictionary and get_product_details.

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 states when to use the tool ('Use this to evaluate schema and content quality'). It does not explicitly name alternatives, but the sibling list implies the appropriate contexts for other tools, making the usage context clear.

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 burden. It discloses that the tool returns SQL for mounting a Dataplex listing and enables quick trial data querying. However, it lacks details on prerequisites like required Snowflake privileges or what the SQL includes, so it provides limited behavioral insight.

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, readable sentence that front-loads the core action. The phrase 'no browser, no sales call' adds a bit of promotional flair but doesn't waste significant space. Slightly trimmed would earn a 5.

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 tool is simple with one parameter and no output schema or annotations. The description explains the overall purpose and usage context, but the missing parameter semantics and lack of any return format details mean an AI agent would struggle to invoke it correctly without further info.

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%, and the description does not explain the 'slug' parameter at all. The agent is given no guidance on what value to provide or how it affects the returned SQL, making this parameter effectively undocumented.

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 tool's function: providing copy-paste SQL to mount a Dataplex listing and query trial data from within Snowflake. It specifies the verb, resource, and scope, and distinguishes itself from siblings by focusing on Snowflake-specific SQL setup.

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 gives clear context for when to use: for an agent inside a Snowflake account, with no browser or sales call needed. However, it doesn't explicitly exclude alternatives or name sibling tools, so it falls short of a perfect score.

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 of disclosure. It states that the tool returns a list and specifies the exact fields (slug, name, one-liner, price, links), plus the data categories covered. This gives a clear behavioral expectation without side effects or hidden requirements, though it does not mention pagination or access constraints.

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, front-loaded sentence that packs in a detailed list of data categories and return fields without any fluff. Every clause adds specific value, making it appropriately sized and highly scannable.

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 list tool with no parameters and no output schema, the description is fully self-contained: it states exactly what is returned and what product domains are included. It answers the likely questions an agent would have before calling it, and no additional context is needed.

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 has zero parameters, and the schema coverage is 100% (vacuously). Since there are no parameters, the description does not need to explain any. The baseline for zero parameters is 4, and the description correctly adds no unnecessary parameter information.

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-resource pairing ('List all Dataplex healthcare data products') and enumerates the product categories and return fields. This clearly distinguishes it from siblings like get_product_details or search_health_datasets, which focus on individual products or searching.

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 phrase 'List all' clearly conveys the intended use case: an overview of all available products without filtering or searching. It provides clear context but does not explicitly mention when not to use it or name alternatives, so it falls 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 the search scope and return of matching products, but it does not clarify the return format, pagination, or any edge cases. This is minimal behavioral context, not contradictory, but could be more transparent.

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 two sentences, with the action verb upfront. It includes illustrative examples and clarifies matching logic without unnecessary padding. Every sentence earns its place, making it concise and well-structured.

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 single-parameter search tool with no output schema, the description provides adequate context on purpose, input semantics, and return type. The main gap is the unspecified format of 'matching products,' but this is a minor omission given the tool's simplicity.

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 only defines 'query' as a string, but the description adds meaning by interpreting it as a search keyword and providing concrete examples (e.g., 'nursing home compliance', 'CDC mortality'). This fully compensates for the 0% schema coverage and clarifies the expected input.

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 tool's function: searching Dataplex healthcare datasets by keyword, with explicit examples. It specifies the match targets (product names, descriptions, table names) and the return type (matching products), which distinguishes it from sibling tools like list_products or get_product_details.

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 for keyword-based search through its examples and phrasing, but it does not explicitly state when to use this tool versus alternatives. It lacks mentions of exclusions or comparisons to siblings like list_products, leaving usage guidance mostly implied.

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