Server Quality Checklist

Tool Scores

• get_industry_employmentA
  4.1/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the burden. It discloses the year constraint (>=1960) and the return format (percentage of total employment), but it does not mention permissions, rate limits, or error behavior. For a simple read query this is moderate, but gaps remain.

  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 very compact, with three clear sections: summary, arguments, returns. Every sentence provides needed information with no redundancy or fluff.

  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?

  The description is complete for a simple two-parameter lookup: it covers input format, output semantics, and a constraint on year. With an output schema existing, it need not explain returns further. Minor gaps include lack of mention of data availability limitations or edge cases, but overall it is sufficient.

  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?

  Schema coverage is 0% (parameters only have titles). The description compensates strongly by providing ISO code format and examples for country, and explicitly stating year must be a four-digit year >=1960. This goes well beyond the schema's minimal information.

  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 explicitly states '특정 국가와 연도의 산업 섹터 고용 비율을 조회합니다' (queries industry sector employment ratio for a specific country and year), which is a specific verb+resource+scope. It clearly distinguishes from sibling tools like agriculture or services employment.

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

  Usage Guidelines3/5

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

  The description implies usage by its specificity to industry sector and the required country/year parameters, but it does not explicitly mention when to use this tool versus sibling alternatives or any exclusions. Since sibling tools exist and are not referenced, guidance is only implied.

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

• get_agriculture_employmentA
  4.3/5.0
  Behavior4/5

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

  With no annotations, the description carries the transparency burden. It discloses that the operation is a read-only query and specifies the return format (percentage of total employment). It also details input constraints (ISO 2/3-letter code, year >= 1960). However, it does not mention data availability, error behavior, or other operational nuances, but for a simple lookup this is largely adequate.

  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 concise and well-structured: a one-sentence purpose statement followed by a clearly formatted Args section and a Returns line. Every sentence carries essential information, with no redundant or filler 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 (only two parameters, no nested objects) and the presence of an output schema, the description covers the core aspects: purpose, parameters, and return semantics. Minor omissions like error handling or data source details are not critical for a basic lookup, but the absence of any usage alternatives slightly reduces completeness.

  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?

  Schema description coverage is 0%, so the description compensates fully. It documents each parameter beyond the schema's type information: country as ISO 2- or 3-letter code with examples, and year as a four-digit year with a minimum constraint. This adds real semantic value for the agent.

  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's function: 'Queries the agricultural sector employment ratio for a specific country and year.' It uses a specific verb (조회합니다/retrieves), identifies the resource (agriculture sector employment rate), and distinguishes itself from sibling tools (get_industry_employment, get_services_employment) by explicitly focusing on agriculture.

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

  Usage Guidelines3/5

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

  The description implies the use case—querying agriculture employment for a country/year—but provides no explicit guidance on when to choose this tool over siblings or when not to use it. There are no exclusion criteria or alternative tool references, so the context is only implicit.

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

• get_services_employmentA
  4.3/5.0
  Behavior4/5

  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 discloses the year constraint (>=1960) and country code format (ISO 2- or 3-letter), and clarifies the output as a percentage of total employment. This provides meaningful behavioral context beyond the schema.

  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 compact and well-structured: a purpose sentence followed by Args and Returns sections. Every sentence adds value, with no redundancy or filler.

  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 query tool with 2 parameters and an existing output schema, the description provides all necessary input constraints and output details. The only gap is the lack of usage comparisons with sibling tools, which is minor given the clear sector-specific purpose.

  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?

  Schema description coverage is 0%, so the description fully compensates by defining the country code format with examples and the year range. It adds critical meaning that the bare schema types ('string', 'integer') lack.

  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 uses the specific verb '조회합니다' (queries) and clearly identifies the resource: service sector employment ratio for a given country and year. The sector-specific focus distinguishes it from sibling tools like get_agriculture_employment and get_industry_employment.

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

  Usage Guidelines3/5

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

  The description does not explicitly state when to use this tool over alternatives or mention exclusions. The naming implies it is for services sector data, but no direct comparison to siblings is provided.

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

• get_employment_by_sectorA
  4.6/5.0
  Behavior4/5

  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 the return format is employment ratio percentages for three sectors and that a message is returned if no data exists. This adds meaningful behavioral context beyond what the schema provides, though it does not mention permissions or error specifics, which are less critical for a read-only query tool.

  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 concise, structured with clearly labeled Args and Returns sections, and contains no wasted words. Every sentence adds useful information, and the use of bullet-style formatting enhances readability.

  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?

  For a simple two-parameter read tool with no annotations, the description is complete: it covers purpose, parameter constraints, return format, and the missing-data case. The sibling tool names and context signals fill in the remaining guidance, making the description sufficient for correct invocation.

  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?

  Schema description coverage is 0%, so the description compensates fully. It defines country as an ISO 2- or 3-letter code with examples, and year as a four-digit number >=1960. These constraints are not present in the schema, making the parameter documentation highly valuable.

  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 it retrieves employment data by sector for a specific country and year, and explicitly lists the three sectors (Services, Agriculture, Industry). This distinguishes it from sibling tools that focus on a single sector, fulfilling the 'specific verb+resource' test.

  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 implies that this tool returns all three sectors at once, contrasting with the sibling tools that likely return data for a single sector. However, it does not explicitly say 'use this when you need all sectors' or name the alternatives, so the guidance is clear but not fully explicit.

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