Server Quality Checklist

Tool Scores

• list_datasetsA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description must indicate behavioral traits. It states 'list all currently loaded datasets' and 'without reloading', implying a read-only, non-destructive operation with minimal impact. It does not mention authentication or side effects, but none are expected for a list operation.

  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 extremely concise: two sentences that state the action and the usage context. No filler or repetition. Every sentence adds value.

  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 has no parameters and an output schema exists (though not shown), the description is sufficient. It specifies the return values (names, row counts, column names). It could mention if there are limits or pagination, but for a simple list, this is adequate.

  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, and schema description coverage is 100%. The description adds no parameter info since none exist, which is appropriate. Baseline for 0 parameters is 4.

  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 currently loaded datasets and specifies the returned information (names, row counts, column names). It distinguishes from sibling tools like load_dataset, which loads data, and explain_segment, which analyzes segments.

  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 includes a usage hint: 'Use this to remind yourself what data is available without reloading.' This implies the tool is for quick lookup, avoiding reloading. However, it does not explicitly state when not to use it or compare to alternatives like load_dataset.

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

• explain_segmentA
  4.7/5.0
  Behavior4/5

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

  No annotations provided, so description carries full burden. It states returns complete JSON in single response and warns against multiple calls. However, it does not explicitly mention whether the tool modifies data or has side effects, though implied read-only.

  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?

  Well-structured with core purpose first, then usage notes, then parameter descriptions. Slightly verbose but every sentence adds value; could be trimmed slightly without loss.

  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 6 parameters (4 required), output schema exists, no nested objects, the description covers all aspects: purpose, usage, parameters, and expected output format (multi-level nested JSON). Complete for a complex analysis tool.

  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%, but the description explains all parameters: dataset_name, target, target_class, condition_variables, attributes, min_base. Provides guidance like excluding direct encodings of target for attributes, and explains condition_variables purpose.

  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 finds drivers of a target outcome within a specific segment using the CLARA method, with a specific verb-resource combination. It distinguishes from sibling tool find_drivers by noting use for local/conditional 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?

  Explicitly says to use find_drivers first for global picture and explain_segment only for drilling into a slice. Also warns to call it once and not multiple times, providing clear when-to-use and when-not-to-use guidance.

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

• load_datasetA
  4.8/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden. It discloses return values (column names, row count, distinct values for categorical columns) and hints at in-memory loading. It does not fully detail side effects or error conditions, but for a load function, this is 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 well-structured: first paragraph for purpose and usage, second for return and metadata application, third for parameters. Every sentence is informative and earns its place, with no redundancy.

  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 complexity (loading datasets), the description covers the essential: input parameters, usage sequence, and output metadata. It integrates with sibling tools (find_drivers, explain_segment) and provides actionable guidance. Output schema exists, but description still explains return values, making it complete.

  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%, so description compensates by explaining each parameter: name as a short label, path as absolute/relative path, separator with default and tab example, encoding with utf-8 default and cp1250 for Windows. This adds significant 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 verb 'load', resource 'CSV or ZIP-compressed CSV file', and the purpose 'into memory under a given name'. It also distinguishes itself from siblings by indicating it should be called ONCE before find_drivers or explain_segment.

  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 states when to call ('ONCE before find_drivers or explain_segment') and that reloading the same file is unnecessary. Provides guidance on using returned metadata for find_drivers, including identifying target variables and exclusion lists.

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

• find_driversA
  4.9/5.0
  Behavior5/5

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

  Despite no annotations, the description fully discloses behavioral traits: it returns a complete nested JSON, automatically runs sub-analysis if needed, explains lift values (Lift > 1 / < 1, strength signs), and details how parameters like max_depth and auto_boundaries affect results. No contradictions with annotations as none exist.

  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 clear sections (purpose, usage admonition, response explanation, important note, lift interpretation, Args) and is front-loaded with critical usage guidance. However, it is somewhat verbose; some details could be condensed without losing value, earning a 4 instead of 5.

  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?

  The description fully covers the tool's behavior given its complexity (8 parameters, nested response). It explains the return structure (drivers list with sub_drivers), lift interpretation, and parameter effects. An output schema exists, so omitting return field details is acceptable. The description is sufficient for correct usage.

  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 adds extensive meaning: each parameter (dataset_name, target, target_class, attributes, filters, min_base, max_depth, auto_boundaries) is explained with purpose, defaults, and usage examples. For instance, attributes is described as a way to exclude redundant columns, and filters includes instructions on value matching.

  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 purpose: 'Find the key drivers and influencers of a target outcome in a loaded dataset.' It specifies the verb ('find'), resource ('drivers and influencers'), and distinguishes from siblings by emphasizing that it returns complete nested JSON in one call, contrasting with potential iterative approaches.

  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 guidance: 'Call this ONCE' and 'Do NOT call it multiple times to refine results.' It instructs when to call again with filters (only for specific sub-segments not already covered) and warns about avoiding trivial drivers by checking load_dataset output. This clearly differentiates usage from alternatives.

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