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 does reveal the return shape ('raw values with year, unit, and unit label') and clarifies that this is actual time-series data, which is useful. However, it does not mention defaults, limitations, or any side effects beyond the obvious read-only nature of 'Get'.

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 compact and front-loaded with the core action and resource. The example adds concrete value without unnecessary verbosity, and every sentence earns its place.

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 output schema, the description adequately covers the purpose, example usage, and return contents. It lacks explicit workflow guidance and default-behavior detail, but the schema fills most gaps, making this reasonably complete for an agent to call 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?

Schema description coverage is 100%, so the schema already explains all five parameters. The description adds context about time-series and raw values, but it does not add parameter-specific meaning beyond what the schema provides; the baseline of 3 applies.

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 names a specific verb ('Get') and resource ('actual data (time series) for a variable in one or more territorial units'), and the GDP example concretely illustrates the intended use. This clearly distinguishes it from the sibling search tools, which retrieve metadata rather than data.

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 a workflow by referencing 'search_units' and 'search_variables' in the schema and by contrasting 'actual data' with search-oriented siblings, but it never explicitly states when to use this tool instead of get_unit_details or search tools. The intended pipeline is inferable rather than stated.

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 behavioral burden. It only restates the action and does not disclose output format, error behavior, permissions, or what 'full details' actually includes.

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 with no filler. Every word contributes to identifying the action and target resource.

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 required parameter, no nested objects, and full schema coverage. However, without an output schema or annotations, the description leaves the response unspecified and provides minimal context for choosing the tool confidently.

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 already documents unit_id as 'Unit ID (12-digit territorial code)' with 100% coverage. The description adds no additional parameter meaning beyond that, so the baseline score of 3 applies.

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 a clear action ('Get full details') on a specific resource ('territorial unit') keyed by ID. It is distinguishable from siblings like search_units, though it does not explicitly contrast itself with them.

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 'by ID' implies the tool should be used when a specific territorial unit identifier is already known. However, it gives no explicit guidance about when to prefer this over search_units or other sibling tools, and it lists no exclusions.

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 behavioral transparency burden. It clearly implies a non-mutating browse/search operation and reveals the key outcome (subject IDs), but it does not describe the return format, pagination, or any side effects beyond what the schema already covers.

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, front-loaded with the action and resource, followed by the practical purpose. No filler or repetition; every phrase earns its place.

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 read-only search tool with no output schema and all parameters optional, the description covers purpose and workflow integration. It stops short of describing the response shape, but 'discover subject IDs' implies the essential return value.

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 the baseline is 3. The description adds strategic context rather than parameter-level details, and the schema already explains name filtering, limit, and parent_id sufficiently.

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 ('Browse or search') and names a concrete resource ('GUS BDL subject categories') with concrete examples. It also states the downstream purpose ('discover subject IDs which then anchor variable searches'), distinguishing it from sibling tools like search_variables and search_units.

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 the tool: to discover subject IDs that lead into variable searches. It does not explicitly name alternatives or state when not to use it, but the intended workflow placement is clear enough for 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 full burden. It adds valuable behavioral context by stating the return value (unit IDs for `get_data`) and by defining level code meanings (0, 2, 5, 6), which explain how the `level` parameter changes results. The word 'Search' implies a read-only operation; however, it does not discuss auth, error behavior, or pagination, slightly limiting transparency.

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 with no filler. The core purpose is front-loaded, the output usage follows, and the level mapping is presented concisely as a legend. Every sentence adds distinct value.

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 search tool with three parameters and no output schema, the description is largely sufficient. It covers what results are for and the meaning of an ambiguous parameter. It could be more complete by stating that omitting `level` searches across all levels or by describing the exact response shape, but these are minor gaps.

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 description adds meaning beyond the schema by decoding the `level` parameter values (0=Kraj, 2=Województwo, etc.), which the schema only describes as 'Optional territorial level (0..7)'. It does not add detail for `limit` or `name`, but the schema already provides adequate descriptions for those.

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 verb ('Search') and resource ('territorial units'), backed by concrete examples (voivodeships, powiats, gminas). It distinguishes itself from siblings by targeting a specific entity type while noting the output feeds `get_data`, which is a distinct purpose from `search_subjects` or `search_variables`.

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 clear context: use this to find territorial unit IDs for use with `get_data`. It implicitly signals when to use this tool versus siblings by focusing on territorial units, though it does not explicitly state when not to use it or mention alternatives by name.

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, and it does provide non-obvious context: variables are atomic units and their IDs feed get_data. However, it does not disclose return format, whether results are partial-name matches, pagination, or limit 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 sentences front-load the core purpose, then add the minimum necessary conceptual and filtering context. No filler or redundant restatement of the name.

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 optional-parameter search tool with no output schema, the description covers what the tool returns conceptually (variable records with IDs), how to filter, and how it fits with get_data. It omits only limit handling and explicit return-field details, which are minor for this class of 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?

Schema coverage is 67%; the schema already documents name and subject_id, and the description adds that subject_id comes from search_subjects and that filters combine with 'and/or'. It adds no semantics for the 'limit' parameter, which remains undocumented in both schema and description.

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 and resource ('Search for variables (data series) in GUS BDL') and defines what a variable is, distinguishing it clearly from search_subjects/search_units by its atomic data-series role and connection to get_data.

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 gives clear context by instructing the user to filter by 'subject_id (from search_subjects) and/or name', which implies the correct workflow of selecting subjects first and then finding variables. It does not explicitly name alternatives or exclusions, 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.