opendata-cat

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

Annotations already indicate safe read operations; the description adds specific metadata content returned, complementing the annotations without contradiction.

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: first specifies output, second gives usage guidance. 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 one well-documented parameter, no output schema needed, and rich annotations, the description fully covers return content and usage context.

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% and includes a detailed description of dataset_id format and examples. The description does not add additional meaning beyond what the schema 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?

The description clearly states the tool returns complete metadata for a dataset, listing specific contents (fields, types, API endpoint, etc.), and explicitly distinguishes it from search_datasets by advising it be called after search_datasets.

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 ('Call after search_datasets') but does not explicitly mention when not to use it or alternative tools like list_dataset_fields for just field info.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds value by detailing the return values (total datasets, count per portal, category list with counts), which enhances transparency beyond 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 two concise sentences, front-loading the purpose and output, with no redundant or 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?

Given zero parameters and no output schema, the description fully covers purpose, usage context, and return details, making it complete for a simple listing 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?

There are no parameters (schema coverage 100%), so the description correctly states 'No parameters required.' With zero parameters, baseline is 4, and the description adds no extra info 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 states the action 'List all dataset categories and themes with counts per portal' with a specific verb and resource, and distinguishes itself from sibling tools by positioning as a discovery step before search_datasets.

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?

Explicitly tells when to use the tool: 'Great first step to discover what data types are available before searching with search_datasets.' Also notes no parameters are required, leaving no ambiguity.

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 provide strong behavioral hints (readOnly, idempotent, non-destructive). The description adds little beyond stating it lists fields. It does not elaborate on potential side effects or edge cases, but given the rich annotations, this is acceptable.

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 no wasted words. It front-loads the action and provides critical usage context efficiently.

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 list tool with one parameter and strong annotations, the description is complete. It provides purpose, usage guidance, and a preview of output fields. Lacks full output details but no output schema is required.

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 already covers the parameter description fully (100% coverage), and the description does not add new information about the parameter. Baseline score of 3 is appropriate.

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 lists fields of a dataset with specific attributes (name, data type, description). It also explicitly distinguishes from sibling tool query_dataset by indicating it should be called first.

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 explicit guidance to call this tool before query_dataset to know available fields and filters. It provides clear usage context but does not mention when not to use it.

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 declare readOnly, openWorld, idempotent, and non-destructive. Description adds concrete details: exactly 13 portals with dataset counts, consistent with hints.

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 main action, followed by specific enumeration. No wasted 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?

Given no parameters and no output schema, the description fully covers what the tool does and what it returns (list of portals with counts). No 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?

No parameters exist; description correctly states 'no parameters required', fulfilling the baseline for 0-param tools.

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?

Clearly states the tool lists 13 indexed open data portals with dataset counts, and enumerates specific portals. Differentiates from sibling tools which focus on dataset-level operations.

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?

States no parameters required and lists included portals, implying use for an overview of available data sources. Could explicitly mention when to use sibling search tools instead, but 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?

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description reinforces this with 'Query real data' but adds little beyond that; it mentions common filter patterns but does not discuss pagination behavior or error handling. Since annotations are present, the description adds modest value.

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 with two sentences, each serving a clear purpose: stating the core action and directing to useful resources. There is no redundancy or unnecessary detail.

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?

Given no output schema, the description could hint at the return format (e.g., rows of data) but does not. It covers core purpose and dataset_id source but lacks details on pagination behavior and output structure. It is adequate but not rich, especially with 5 parameters and nested objects.

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%, so all parameters are fully described in the schema. The description does not add parameter-specific meaning beyond the schema; it only provides contextual examples of filter patterns. Baseline 3 is appropriate as the description adds no new syntactic 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 clearly states the action ('Query real data') and the resource ('a dataset'), distinguishing it from sibling tools like search_datasets (which finds datasets) and list_dataset_fields (which lists fields). It also hints at specific use cases with filter patterns, though it could be more explicit about returning rows of 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 that the dataset_id comes from search_datasets, suggesting a tool chaining workflow, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. The mention of 'Check instructions' is vague and does not offer clear guidance on when to choose this tool over siblings.

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety. The description adds value by explaining how relations are computed ('thematic similarity across portals'), which is behavioral context not in 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 with two sentences. It front-loads the purpose and provides a concrete example without unnecessary words. Every sentence adds 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?

Given the tool's simplicity (one parameter, no output schema) and rich annotations, the description covers the essential aspects: purpose, cross-portal scope, and thematic similarity. It could be more explicit about the output format, but the example implies a list of related datasets.

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 provides a clear description for dataset_id (100% coverage). The tool description does not add additional meaning beyond the schema, meeting the baseline expectation.

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: finding related datasets from other portals. It uses a specific verb ('find') and resource ('related datasets'), and distinguishes from sibling tools like search_datasets by emphasizing cross-portal relations.

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 a usage example ('air quality...') and states it's for discovering complementary data. While it doesn't explicitly list when not to use, the context implies it's best when exploring cross-portal connections. Lacks explicit alternatives but is still helpful.

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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context by emphasizing the importance of checking server instructions before searching. This is consistent with annotations and provides additional guidance, though no major behavioral traits beyond annotations are disclosed.

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 exceptionally concise at two sentences. The first sentence front-loads the core purpose, the second provides a crucial usage guideline. Every word earns its place with no redundancy or filler.

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 what the tool does and when to use it. With no output schema, it could benefit from mentioning the return type (e.g., list of dataset objects). However, the sibling tools are listed, and the schema covers parameters thoroughly. Overall, it is fairly complete but has a minor gap in return value description.

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%, with all parameters well-described in the input schema. The description itself adds minimal parameter-level meaning beyond the schema, such as the general 'free text' mention. Given high coverage, baseline is 3; the description does not significantly enhance parameter understanding.

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 'Search datasets by free text', providing a specific verb and resource. It distinguishes itself from the sibling tool 'query_dataset' by noting that many queries have a direct dataset_id, which should be used instead. This makes the purpose unambiguous and distinguishes it from alternatives.

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 when to use this tool: 'Only use search_datasets when you don't know which dataset you need.' It also instructs to check server instructions first and mentions the alternative 'query_dataset' for direct lookups. This provides excellent guidance on usage context and alternatives.

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 declare readOnlyHint=true, idempotentHint=true, and the description adds behavioral context: it explains search scope (titles, descriptions with summaries), expected result types, and gives a critical usage instruction about citing and linking. No contradiction with 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 a single paragraph but efficiently packs information: purpose, search scope, counts, filtering details, and a vital usage note. It is front-loaded with the main action. Slightly more structure could improve readability, but it remains concise and value-dense.

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?

Given no output schema, the description adequately covers search features, fields searched, filtering options (including year nuance), and important citation rules. It lacks details on result format, but per rules, return values need not be explained. Overall, it provides sufficient context for effective use.

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%, but the description enriches parameter semantics with examples (e.g., query examples like 'visita papa'), clarifies the description field's content, provides guidance on year filtering limitations, and explains filter options. This goes well beyond the schema's basic descriptions.

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 it searches radio shows, episodes, and people from the radioteca.cat archive, specifying what fields are searched and the document counts. It distinguishes itself from sibling tools, which are dataset-oriented, making its purpose unambiguous.

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 indicates when to use this tool for searching Catalan radio content. While it doesn't explicitly exclude alternatives, the sibling tools are unrelated (dataset queries), so usage context is clear. No direct guidance on when not to use it, but sufficient for differentiation.

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