Server Quality Checklist

Tool Scores

• describe_schemaB
  3.2/5.0
  Behavior2/5

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

  No annotations are provided, so the description alone must disclose behavioral traits. It states the return value but does not mention that the operation is read-only, safe, or free of side effects. The minimal description lacks transparency about non-obvious behaviors like required privileges or potential errors.

  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 a single, front-loaded sentence with no filler. Every word contributes to the purpose: 'Return column names and types for a table so the agent can write SQL.' This is the ideal length for a simple tool with one parameter.

  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?

  Given the tool's simplicity (one parameter, no output schema), the description is mostly sufficient. However, it omits details about the return format (e.g., list of objects with 'name' and 'type') and does not mention that the table must exist. The description is adequate but not thorough.

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

  Parameters1/5

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

  The input schema has 0% description coverage, and the tool description does not explain the 'table' parameter (e.g., expected format, case sensitivity, whether full path is needed). The description adds no meaning beyond the schema, failing to compensate for the lack of parameter documentation.

  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 column names and types for a table, with the explicit purpose of enabling SQL writing. It distinguishes itself from siblings like list_datasets (listing tables) and query_sql (executing queries) by focusing on schema introspection.

  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 for writing SQL but does not explicitly state when to use versus siblings (e.g., 'Use before writing queries' or 'Do not use for listing tables'). No exclusions or alternatives are given, leaving the agent to infer context from sibling tool names.

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

• price_statsA
  3.7/5.0
  Behavior2/5

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

  No annotations are provided, so the description bears the full burden of behavioral disclosure. It only states the tool computes statistics, without mentioning whether it is read-only, requires permissions, or any side effects. For a simple aggregation tool, the lack of explicit safety cues is a minor gap.

  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 with no redundancy. The key function (aggregates of price_manwon) is front-loaded, and the convenience aspect is stated 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?

  Given the tool's simplicity (2 params, no output schema, no annotations), the description covers the core function and rationale. However, it omits the return format, which could be helpful for an agent deciding how to use results. Still, it is mostly complete for the intended use case.

  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?

  The input schema has 0% description coverage, and the description only indirectly explains parameters by mentioning 'grouped by a column' and the default column 'sigungu'. It does not describe the 'table' parameter or valid values, leaving the agent to infer details. The description adds some meaning but is insufficient for full understanding.

  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 computes average, min, max, and count of price_manwon grouped by a column. It distinguishes itself from siblings like query_sql by being a specialized convenience tool, avoiding hand-written SQL.

  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 use for simple aggregation tasks on price_manwon, suggesting it saves effort compared to writing SQL. However, it does not explicitly state when not to use it or mention alternatives like query_sql for more complex queries.

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

• correlateA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description discloses the join on shared keys, return of correlation and sample size, and schema validation. However, it does not detail side effects, permission requirements, or error handling, which is slightly incomplete.

  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 (4 sentences), front-loads the core purpose, and includes an illustrative example. Every sentence adds value without redundancy.

  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 complexity (cross-table join correlation), the description covers essential aspects: statistical method, join keys, return values, and validation. It lacks details on handling missing data or edge cases, but overall is sufficient.

  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 0%, but the description explains the overall function and mentions shared keys and numeric columns, yet does not individually describe each parameter or their constraints beyond the implicit requirement that col_a/col_b are numeric and exist in their respective tables.

  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 computes Pearson correlation between two numeric columns across two datasets, with specific verbs and resources. It distinguishes from sibling tools like query_sql or price_stats by its focused analytical purpose.

  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 an illustrative example (correlating wind speed with PM10) that helps understand when to use the tool, but does not explicitly state when not to use it or compare to alternatives like query_sql.

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

• query_sqlA
  4.4/5.0
  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 read-only behavior, statement type restrictions, and row capping. While it could mention error handling or auth needs, the current disclosure is above average for a simple SQL 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?

  Three short sentences, front-loaded with the main purpose. Every sentence adds value without redundancy. Highly efficient and easy to parse.

  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 tool with one parameter and no output schema, the description covers input constraints, usage context, and a prerequisite sibling. It hints at the output (tabular result) but could specify the row cap value or clarify pagination. Still, it's largely complete for typical use.

  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 single parameter 'sql' has 0% schematic description coverage, so the description must compensate. It clarifies allowed SQL constructs (SELECT/WITH) and implies a row limit, but lacks syntax examples or format details. This is adequate but not exceptional.

  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 runs a 'read-only SELECT' against the 'mart,' specifying the verb and resource. It distinguishes itself from sibling tools like 'describe_schema' and 'list_datasets' by focusing on querying rather than metadata or analysis.

  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?

  The description provides explicit when-to-use guidance ('Run a read-only SELECT'), what is not allowed ('Only single SELECT / WITH statements'), and a key alternative ('Use describe_schema to learn column names before querying'). This helps the agent choose correctly.

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

• list_datasetsA
  4.5/5.0
  Behavior4/5

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

  With no annotations, the description correctly indicates it is a read-only listing operation. The output schema provides further clarity, though it does not explicitly state side effects.

  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 a single, efficient sentence that conveys all necessary information without extraneous content.

  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 no parameters, an output schema, and clear sibling context, the description provides complete information for an agent to use the tool 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?

  There are no parameters, so the schema coverage is 100%, meeting the baseline. The description does not need to add parameter details.

  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 lists datasets with their title and description, distinguishing it from sibling tools that focus on schema description, querying, or statistics.

  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 tool's purpose is obvious for listing available datasets, and given the sibling tools are for different operations, usage guidance is clear without explicit exclusions.

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