Server Quality Checklist

Tool Scores

• read_employee_salaryA
  3.5/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the behavioral disclosure burden. 'Read' implies a non-mutating operation, and 'latest rows' suggests ordering by recency, but the description does not reveal limits on result size, pagination behavior, or any other side effects or constraints.

  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 short sentences with no filler. The primary action is front-loaded, and the secondary sentence provides useful usage guidance without redundancy.

  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?

  For a simple read tool with one optional parameter and an output schema, the description is reasonably complete. However, it leaves the limit parameter semantics and the relationship to query_employee_salary implicit, so an agent must infer when this tool is the right choice.

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

  Parameters2/5

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

  Schema description coverage is 0%, and the description does not explicitly explain the 'limit' parameter or how it relates to 'latest rows.' The phrase 'Keep the result small' indirectly implies limiting output, but it does not add clear parameter-level meaning beyond the schema's title and default.

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

  Purpose4/5

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

  The description clearly states the action ('Read') and the resource ('employee_salary'), and narrows the scope to 'latest rows,' which helps distinguish it from the more general query_employee_salary sibling. However, it does not explicitly name or contrast the sibling tools, so it stops short of full differentiation.

  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 instruction 'Keep the result small for LLM use' gives clear practical context for how the tool should be invoked. It does not explicitly state when to choose this tool over query_employee_salary or get_employee_salary_schema, so there is no exclusion guidance.

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

• get_employee_salary_schemaA
  3.7/5.0
  Behavior3/5

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

  The description indicates a read-only metadata retrieval operation ('Return the schema') and has no annotations to contradict this. However, it offers no additional behavioral context, such as whether the schema is cached, whether authentication is required, or any limitations. For such a simple tool, the basic behavior is adequately disclosed.

  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 a single concise sentence with the key action and resource front-loaded. The phrase 'for AI/agent use' is mildly redundant because the tool is already defined in an agent context, which prevents a perfect score.

  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 zero-parameter metadata tool with a provided output schema, the description sufficiently states the returned object. It lacks explicit guidance on when to call it relative to sibling tools, but the simplicity of the operation and the presence of an output schema make the definition essentially complete.

  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 schema naturally covers all parameter semantics (100% coverage). Per the baseline for 0-parameter tools, the description does not need to add parameter details and none are missing.

  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 states a specific verb ('Return') and resource ('schema of the employee_salary table'), making the tool's function unambiguous. It clearly differs from sibling tools read_employee_salary and query_employee_salary, which would fetch data rather than metadata.

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

  Usage Guidelines2/5

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

  No guidance is given on when to use this tool versus the sibling read/query tools. The agent must infer from the tool name and context that this is the metadata lookup and the others are data access, which is not explicit.

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

• query_employee_salaryA
  3.9/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden of disclosing behavior. It explicitly states the operation is read-only and limits input to SELECT statements, which is the most important safety behavior. It does not detail output formatting or query limits, but the available output schema helps cover return-value expectations.

  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 short sentences with no filler. It front-loads the core action and constraint immediately, and every sentence adds meaningful guidance.

  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 single-parameter tool with an output schema, the description is largely sufficient: it names the target table, restricts the query type, and clarifies read-only intent. It could be more complete by mentioning sibling-tool usage or query limits, but those are secondary for correct invocation.

  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 input schema provides only the parameter name 'sql' with no description, so schema coverage is 0%. The description compensates by clarifying that the sql parameter is a SQL query, must target the employee_salary table, and must be a SELECT statement. This gives the agent enough semantic grounding to construct the argument.

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

  Purpose4/5

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

  The description clearly states the verb and resource: run a read-only SQL query against the employee_salary table. It also specifies that only SELECT statements are allowed, which gives concrete scope. However, it does not explicitly differentiate itself from sibling tools like read_employee_salary or get_employee_salary_schema.

  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 this tool is for running ad-hoc read-only SQL queries against the employee_salary table. It provides a meaningful constraint ('Only SELECT statements are allowed') but gives no explicit guidance on when to choose this over read_employee_salary or get_employee_salary_schema.

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