Server Quality Checklist

Tool Scores

• detect_missing_valuesA
  3.7/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden. It does explain the output behavior (counts, percentages, sorting by most-missing), which is useful. However, it does not disclose any side effects, limitations, or assumptions (e.g., whether all columns are included, handling of empty files), leaving some behavioral aspects unexplored.

  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, concise sentence that starts with the action verb 'Report' and includes the key details (counts, percentages, sorting). Every word earns its place, and there is no fluff or 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?

  For a simple one-parameter tool with an output schema, the description provides sufficient information about what it does and what it returns. It is reasonably complete despite the lack of parameter explanation, because the parameter is self-evident from the context. Slightly higher than mid-tier because the output behavior is clearly specified.

  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 schema has one required parameter 'filename' with 0% description coverage, and the tool description does not mention it at all. While the parameter's purpose is inferable from the tool name, the description does not add any extra meaning, such as accepted file formats or how the filename is used, which is a gap given the low schema coverage.

  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: 'Report missing-value counts and percentages per column, sorted by the most-missing first.' This is a specific verb ('report') with a clear resource (missing-value stats per column) and unique sorting behavior, distinguishing it from sibling tools like profile_dataset or plot_distribution.

  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 when to use the tool (when you need missing-value insights per column) but does not explicitly mention when not to use it or suggest alternatives. There is no comparison to sibling tools, so usage guidance is inferred rather than explicit.

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

• list_datasetsA
  3.8/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the burden of explaining behavior. It discloses the location and file type (CSV under datasets/) but does not mention whether listing is recursive, how results are ordered, or error behavior. The presence of an output schema mitigates the lack of return-value details, but other behavioral aspects remain undisclosed.

  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 front-loads the action. Every word contributes meaning, 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 zero-parameter tool with an output schema, the description is largely complete. It specifies the source directory and file type. It could add a note about when to use it (e.g., before profiling), but that gap is more relevant to usage guidelines.

  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 zero parameters, so the schema coverage is vacuously 100%. The description adds nothing beyond the schema, but the baseline for zero-parameter tools is 4, and no additional parameter semantics are needed.

  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 action ('List') and the precise resource ('CSV datasets available under the datasets/ directory'). It distinguishes itself from sibling tools like list_models and download_kaggle_dataset by focusing on local datasets.

  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 or how it relates to alternatives. The description only states what it does, leaving the agent to infer that it is a preliminary step before other dataset operations.

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

• plot_distributionA
  4.1/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden and does a good job: it discloses the side effect of saving a PNG to outputs/, conditional behavior for numeric vs categorical columns, and the return of the saved file path. It could add overwrite behavior or failure modes, but the main behavioral traits are covered.

  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 three sentences, each earning its place: purpose, behavior, and return value. It is front-loaded with the primary action and contains no fluff or repetition.

  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 and the existence of an output schema, the description covers the main functionality, side effects, and return value well. However, the lack of explanation for the 'bins' parameter and the ambiguity around the 'filename' parameter slightly reduce completeness. Overall, it is adequate for typical use cases.

  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%, so the description must compensate. It implicitly defines 'column' (the target column) but does not mention 'bins' at all, and 'filename' is only implied via 'save it as a PNG in outputs/'. The description fails to explain the meaning of two of three parameters, which is a significant gap.

  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 a specific verb ('Render') and resource ('distribution chart for a column'), and clearly distinguishes itself from siblings by focusing on single-column distribution visualization. It also states the output (PNG in outputs/) and the return value (file path), leaving no ambiguity about what the tool does.

  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 clearly implies usage: plotting a column's distribution. However, it does not explicitly mention alternatives or state when NOT to use this tool versus siblings like profile_dataset. The context is clear enough to differentiate, but lacks explicit exclusions.

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

• list_modelsA
  4.3/5.0
  Behavior4/5

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

  No annotations are present, so the description carries the full burden. The verb 'List' indicates a read-only operation, and 'saved under models/' gives behavioral context about the data source. It also discloses that the output includes target column and task type, but does not explicitly mention side-effect-free behavior or error cases.

  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 that conveys all necessary information without redundancy. Every word earns its place.

  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 parameterless listing tool with an output schema, the description is sufficient. It states the action, the resource location, and the specific fields returned, leaving no significant gaps for the agent to resolve.

  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 has zero parameters, so the baseline is 4. The description adds no parameter-specific meaning (none needed) and correctly focuses on what the tool returns.

  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 'List' with the resource 'trained models' and provides clear scope ('saved under models/') and output content ('target column and task type'). This clearly differentiates it from sibling tools like list_datasets, which lists datasets.

  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 viewing existing trained models before prediction or training, but does not explicitly state when to use it or mention alternatives. The contrast with list_datasets is clear from the name alone but not articulated in the description.

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

• profile_datasetA
  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 transparency burden. It explicitly specifies the outputs (shape, dtypes, numeric stats, sample rows) and the constraint that filename must be a CSV inside datasets/, which adds meaningful behavioral context. However, it does not mention side effects (e.g., read-only behavior) or error handling, leaving some aspects implicit.

  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 front-loaded with the main action, followed by a minimal Args block. Every sentence adds value—there is no fluff or repetition of schema fields, making it an efficiently structured docstring.

  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 profiling tool, the description covers purpose, parameters, and output details. The presence of an output schema handles return structure, and the description adds the necessary file-location constraint. This is complete enough for an agent to select and invoke 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?

  Both parameters are explained in the Args section with more context than the schema: filename specifies a CSV inside datasets/ with an example, and sample_rows specifies how many head rows to include. Since schema descriptions are absent (0% coverage), the description fully compensates by providing essential 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 opens with 'Profile a dataset' and enumerates exactly what profiling includes: shape, dtypes, numeric stats, and row sample. This clearly distinguishes it from sibling tools like list_datasets (which lists datasets) or plot_distribution (which visualizes), making the tool's purpose unmistakable.

  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?

  No explicit usage context is provided—the description doesn't say when to choose profiling over detect_missing_values or list_datasets. The purpose implies initial data exploration, but there is no when/when-not guidance or named alternatives, leaving the usage scenario to be inferred.

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

• download_kaggle_datasetA
  4.5/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden and does disclose key behavioral traits: use of kagglehub, copying CSVs into datasets/, and authentication requirements. It does not mention overwrite behavior or error handling, but the main side effects are clearly stated.

  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 concise sentences, front-loading the main action and then adding necessary prerequisites and an example. Every sentence serves a purpose with no fluff.

  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 simplicity (one parameter) and the presence of an output schema, the description covers everything essential: purpose, side effect, authentication, and input format. Minor details like non-CSV handling are implicit in the phrase 'copy any CSVs', so no critical gaps remain.

  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 has no descriptions for the single 'dataset' parameter, so the description compensates by providing an example slug ('yasserh/titanic-dataset'), which clarifies the expected format. This gives meaningful guidance beyond the schema's bare parameter name.

  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 downloads a dataset from Kaggle via kagglehub and copies CSVs into datasets/, using a specific verb and resource. It implicitly distinguishes from siblings like list_datasets and profile_dataset by focusing on the actual download and file-copy action.

  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?

  It provides clear context that Kaggle authentication is required and gives an example dataset slug format, helping the agent understand prerequisites and input format. However, it does not explicitly mention when to use this tool versus alternatives, though the purpose is obvious.

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

• train_modelA
  4.6/5.0
  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 transparency burden. It discloses key behaviors: one-hot encoding, imputation, scaling within a Pipeline, and persistence to models/. It also explains task inference for the 'auto' value. Missing details like overwrite behavior are not stated, but the core side effects and preprocessing are well covered.

  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 front-loaded with the main purpose, followed by a compact preprocessing note and a clear Args list. Every sentence adds value, and the Args section is necessary given the empty schema descriptions. No fluff or 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?

  The tool is a moderately complex training operation with 6 parameters, but the description covers all of them and explains the preprocessing steps. Since an output schema exists, the absence of return-value details is acceptable. The description provides a complete picture for selecting and 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?

  Schema description coverage is 0%, but the description's Args section explains every parameter: filename, target, features, task, test_size, random_state. This fully compensates for the lack of schema descriptions and provides meaning well beyond parameter names/types.

  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?

  Description opens with 'Train a baseline scikit-learn model, evaluate it on a held-out split, and persist it to models/' which clearly identifies the action, resource, and outcome. It is fully distinguished from sibling tools like predict, list_models, profile_dataset, etc., which cover other stages of the ML workflow.

  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 gives clear context of what the tool does and states it 'works on raw Kaggle CSVs', implying it is suitable for end-to-end training from raw data. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to know when to invoke it.

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

• predictA
  4.8/5.0
  Behavior5/5

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

  No annotations exist, so the description carries full responsibility. It discloses that the model is a full pipeline (automatic missing value/categorical handling), provides per-class probabilities and confidence for classifiers, notes the save_csv side effect (writes to outputs/), and specifies top_n limits the response size. This goes well beyond a bare 'predict' description.

  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-organized: starts with a one-sentence purpose, then explains input options, pipeline behavior, classifier output, and ends with a concise Args list. Each sentence carries useful information without fluff, and the structure makes it easy to scan.

  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 (6 params, two input paths, side effects), the description covers all essential aspects: how to specify the model, how to provide data inline or via file, what happens with preprocessing, output contents (probabilities/confidence), optional CSV saving, and response limiting. The existence of an output schema reduces the need to describe return values, but the description still mentions extras like confidence scores.

  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%, and the description compensates fully by explaining each parameter's purpose and format: model (with/without .joblib), records (list of dicts with example), filename (CSV in datasets/), id_column (echo row IDs), save_csv (write to outputs/), and top_n (max rows). 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 opens with 'Score new data with a previously trained model (from train_model)', clearly stating a specific verb, resource, and origin. It distinguishes itself from siblings like list_models and train_model by focusing on the inference step.

  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?

  It references 'train_model' and 'list_models' to place the tool in the workflow, and explains the two mutually exclusive input methods (records vs filename). However, it does not explicitly state when not to use it or compare it with alternative tools for similar tasks.

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