Server Quality Checklist

Tool Scores

• getDatasetDataA
  3.9/5.0
  Behavior3/5

  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 this is a read-only 'fetch' operation, returns a labelled table, and mentions the 'flat flavour' of the API. However, it does not mention pagination behavior, error handling, rate limits, or other side effects, which would be valuable for an agent.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is exactly two sentences, front-loaded with the core purpose ('Fetches official observations') and includes a key usage instruction (always cite). It is concise with no redundant or filler content.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness3/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  The tool has no output schema, so the description must convey what the return looks like. It says 'labelled table' but does not specify columns or structure beyond that. For a data-fetching tool, this is a moderate gap; more detail about the table's content (e.g., periods, values, dimensions) would improve completeness.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema has 100% description coverage for all five parameters, so the schema already documents each parameter's meaning. The description adds no extra parameter-level detail, which is acceptable given the high schema coverage.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool fetches official observations for one FCSC dataset from the UAE statistics SDMX API (flat flavour) and returns a labelled table. This distinguishes it from sibling tools listDatasets and getDatasetStructure, which list datasets and retrieve structure respectively.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides clear context: use this tool to fetch observations for a specific dataset, and there is an implicit instruction to use listDatasets to find the datasetId. It also includes a citation requirement. However, it does not explicitly name alternatives or exclusion criteria, 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.

• getDatasetStructureA
  4/5.0
  Behavior3/5

  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 communicates that the tool 'Returns' data, implying a read-only operation, and mentions practical uses (building keys, explaining breakdowns). However, it does not explicitly state side-effect-free behavior or provide details on error handling or required permissions, 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.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is two sentences, both informative and free of fluff. It front-loads the primary action ('Returns the dimensions and codelists') and then provides usage guidance, achieving maximum efficiency with zero wasteful content.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  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, no annotations), the description adequately conveys its purpose and usage. It explains what the tool returns (dimensions and codelists) and why you would use it. A brief mention of the response structure could enhance completeness, but it's not essential for such a straightforward tool.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema already provides a full description of the single parameter `datasetId`, including an example, achieving 100% schema description coverage. The tool description adds marginal context by linking the parameter to building a `key` for `getDatasetData` and referencing `listDatasets`, but it does not significantly expand on the parameter's meaning beyond the schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool 'Returns the dimensions and codelists for one FCSC dataset', identifying both the action and the resource. It distinguishes itself from siblings by specifying it works on a single dataset's structure rather than listing datasets or retrieving data.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description gives explicit usage guidance: 'Use this when you need to build a filtered `key` for getDatasetData, or to explain what breakdowns a dataset offers.' It clearly indicates the intended use case, though it does not explicitly state when not to use it or name alternative tools directly.

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

• listDatasetsA
  4/5.0
  Behavior3/5

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

  With no annotations, the description must convey behavioral context. It states the tool lists datasets and supports filtering, and implies read-only operation. Yet it does not disclose pagination, response format, or other potential quirks. The mention of datasetId provides some return context.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness5/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  Two sentences, front-loaded with the core purpose, and zero wasted words. The guidance about finding datasetId is included efficiently.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness4/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple list tool, the description covers the purpose, filtering capability, and its role in the workflow (finding datasetId). While no output schema exists, the description gives enough context for an agent to use it correctly. It could mention pagination or result limits, but overall it is adequate.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100% with descriptive parameter definitions. The description adds no extra meaning beyond that, so the baseline of 3 is appropriate.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose5/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool lists official UAE statistics datasets from FCSC with optional filters. It also distinguishes from sibling tools by positioning it as the first step to obtain a datasetId for getDatasetData.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly instructs to use this tool first to find the datasetId for getDatasetData. However, it does not mention when not to use it or contrast with getDatasetStructure, so it falls short of full 5.

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