epitaxy

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 lists what the tool returns, which implies a read-only check, but it does not disclose edge cases, error behavior, or how to interpret the returned values. Adequate but with notable gaps.

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 short sentences with no filler. The first sentence states the outputs, and the second gives practical usage guidance. Every word earns its place and is appropriately 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?

For a zero-parameter freshness check with no output schema, the description provides the essential context: what it returns and when to call it. It could add format details for the returned status or timestamp, but the tool is simple enough that this is a minor gap.

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 is empty, so there is nothing for the description to add about parameter meaning. Baseline for 0-parameter tools is 4, and no parameter explanation 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 uses a specific verb ('Vrati' - returns) and names the exact resource: time of last data activation, number of live segments, and status of the last run. This makes it clearly distinct from sibling tools like list_drug_recalls or get_supplier_exposure, which cover different domains.

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 tells the agent when to call it: first, when wanting to know if data are fresh ('Volaj to prve, ked chces vediet, ci su data cerstve'). It does not mention when not to use it or name specific alternatives, but the usage context is 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 present, the description takes on the transparency burden and does so well: it discloses confidence levels, match_method presence, the systematic impossibility of exact cross-source matches due to missing FDA/USAspending identifiers, and default keyless behavior. It could still be more complete about pagination, evidence payload, and output fields, but the disclosed caveats are substantial.

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?

Each sentence of the Slovak description carries a distinct point: the join purpose, row semantics, cross-source limitation, the exact claim made, the non-claim, and the default keyless behavior. There is no filler, though the lack of any structural formatting and the dense caveat stacking keep it from 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 description is complete on domain semantics but not on call mechanics. An agent still does not know which identifier to pass for a targeted lookup, what min_confidence changes about filtering, or what the returned rows look like beyond confidence and match_method. With no output schema, those gaps are meaningful.

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?

Because schema description coverage is 0%, the description needed to explain the four params. It adds meaning about confidence values and about keyless behavior (exact/probable, max 20 rows), which helps with min_confidence and limit. However, it never names award_id or company_id as the 'kľúč', nor explains how min_confidence and limit actually affect results.

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 a clear verb-resource pairing: it joins contracts with shortages/recalls and then defines the concrete claim it produces ('this government supplier has an active FDA shortage or recall'). It also distinguishes itself from sibling list tools by stressing it does NOT assert that a specific contract supplies a specific drug.

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 strong interpretive context (cross-source matches are never exact, keyless calls return only exact/probable up to 20 rows) and warns against over-reading the result as a contract-drug link. However, it never explicitly says when to prefer this tool over the sibling tools such as list_drug_recalls or list_federal_drug_contracts; the routing is only implied.

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?

There are no annotations, so the description carries the full burden of behavioral disclosure. It mentions the FDA source and filter names but does not disclose read-only behavior, default ordering, pagination, response format, or any other runtime characteristics. This is too sparse for a tool with no annotation support.

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 short sentence with no filler, and the purpose is front-loaded before the filter list. It is concise, though the brevity contributes to missing essential context in other dimensions.

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, no required parameters, and 0% schema description coverage, this description is too thin. An agent cannot determine expected parameter values, defaults, result shape, or constraints, so it is not fully usable without external knowledge.

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 must compensate, but it merely repeats the parameter names without adding meaning. 'classification' is ambiguous (recall class, status, product category?), and 'company_id' and 'limit' lack format, constraints, or example values.

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 resource as FDA drug recalls and lists the available filters, making the core purpose understandable. It does not use an explicit verb, but the noun phrase plus the tool name is sufficient to distinguish it from sibling tools like list_drug_shortages and list_federal_drug_contracts.

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?

No guidance is provided about when to use this tool versus the sibling tools. The description only states what the tool does and lists filters, leaving an agent to infer appropriate usage from the tool name alone.

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?

There are no annotations, so the description carries the full disclosure burden. It only names the data source and filter parameters, but does not explain what the tool returns, whether it is read-only, what defaults apply, or how filters behave.

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 extremely compact: one purpose sentence plus a filter list. It is front-loaded with the core meaning and contains no unnecessary words.

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 no parameter semantics, an agent cannot determine the response format, valid status or company_id values, or the default limit behavior. The description is too minimal to be safe for correct invocation.

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 needed to compensate. However, it merely repeats the parameter names from the schema ('status, generic_name, company_id, limit') after the word 'Filtre'. It adds no details about allowed values, defaults, or filter combination semantics.

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 states the resource and source: 'Vypadky liekov podla FDA' (medicine shortages according to FDA), and lists the available filters. It can be distinguished from sibling tools like list_drug_recalls by the 'shortages' focus and FDA source, though it lacks an explicit verb and relies on the tool name to indicate the action.

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?

No guidance is given on when to use this tool versus alternatives such as list_drug_recalls or list_federal_drug_contracts. The description simply says what the tool is about, not under which conditions it should be selected.

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 must carry the full transparency burden, but it only names the resource and filters. It does not disclose return format, pagination behavior, data freshness, authorization requirements, or whether the 'list' is limited in any way. The read-only nature is implied by the verb but not explicitly stated.

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 short, with the resource and PSC code placed first and no wasted words. It is appropriately sized for a simple lookup, though it sacrifices useful detail in the same space.

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 no annotations and no output schema, this description is too sparse. An agent cannot determine what fields the response contains, how to interpret PSC 6505, whether filters are optional, or what the default dataset covers. It functions more as a label than a complete usage guide.

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 must compensate, but it only restates the parameter names as 'filtre' without explaining what agency, company_id, or limit mean, their formats, or how filters combine. The minimal addition is clarifying that these parameters narrow results.

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 a specific verb ('list') and resource ('federal drug contracts') with an additional code (PSC 6505) that makes the scope precise. The subject matter is clearly distinct from siblings like list_drug_recalls and list_drug_shortages, so confusion is unlikely even without explicit sibling comparisons.

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?

No guidance is given about when to use this tool versus alternatives. The description merely lists filter parameters and provides no context, exclusions, or routing cues relative to sibling tools such as get_supplier_exposure or list_drug_shortages.

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