Server Quality Checklist

Tool Scores

• describe_latam_synth_datasetA
  4.7/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds behavioral context by stating 'performs no generation' and specifying the return items including the privacy policy, which goes beyond what annotations alone convey.

  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 appropriately sized, with the purpose front-loaded in the first sentence, usage guidance in the second, and a structured 'Returns' block for the output. Every sentence adds value and there is no wasted text.

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

  Completeness5/5

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

  Given the tool has no parameters and an output schema exists, the description is complete. It clearly states what the tool does, when to use it, and what it returns, including a notable privacy policy detail. Nothing essential is missing for an agent to call it correctly.

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

  Parameters4/5

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

  The tool has zero parameters, so the baseline is 4. The description redundantly states 'takes no arguments', but this reinforces the fact that no input is needed, which is helpful for an agent. No parameter documentation is required.

  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?

  States a specific verb 'Describe' and a specific resource 'schema and calibration of the LatAm Synth dataset'. It explicitly distinguishes itself from the sibling generate_latam_financial_data by saying 'It performs no generation', making the purpose immediately clear.

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

  Usage Guidelines5/5

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

  Explicitly tells when to use the tool: 'before generating data' to inspect available tables, columns, goal categories, and countries. It also states exclusions: 'It performs no generation' and 'takes no arguments'. This provides clear context and indirectly points to the generation sibling as the alternative.

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

• generate_latam_financial_dataA
  4.7/5.0
  Behavior5/5

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

  The description adds substantial behavioral context beyond the annotations: it states the output is 'fully synthetic', 'sampled from aggregate distributions', contains 'no personally identifiable information and no real records', and that 'the same seed and parameters always return the same dataset.' This goes far beyond the readOnlyHint and idempotentHint annotations by explaining what the data actually is and how reproducibility works.

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

  Conciseness4/5

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

  The description is well-structured with a summary sentence, a usage paragraph, a structured Args list, and a Returns note. It is slightly verbose in the usage paragraph (listing many use cases) but every sentence contributes value, and the most important information (what it does, key constraints) is front-loaded.

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

  Completeness5/5

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

  Given the tool's moderate complexity (five optional parameters, one sibling tool, output schema available), the description is complete. It explains the purpose, when to use it, all parameters with examples, the synthetic nature, and the return shape. There are no obvious gaps that would prevent an agent from invoking the tool correctly.

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

  Parameters5/5

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

  With 0% schema description coverage, the description fully carries the burden of explaining all five parameters. It provides detailed meaning and usage for each: users range '1 to 200', seed guarantees reproducibility, countries gives an example list, and both date parameters specify ISO format with examples. This is exemplary compensation for the absence of schema descriptions.

  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 opens with a clear statement of what the tool does: 'Generate privacy-safe synthetic financial data for Latin America.' It then specifies the exact resources produced (financial users, savings goals, transactions), which distinguishes it from the sibling describe_latam_synth_dataset that describes rather than generates.

  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 explicitly states when to use the tool: 'Use this tool when you need synthetic financial users, savings goals and transactions for Latin American fintech testing, QA, demos...' This provides clear context, but it does not mention any alternative tool or describe situations where it should NOT be used, so it falls just short of full marks.

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