Server Quality Checklist

Tool Scores

• csv_group_byC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions grouping and aggregation but lacks details on permissions, file format requirements, error handling, or output format. For a tool that processes files and performs computations, this leaves significant gaps in understanding its behavior.

  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 with zero waste. It front-loads the core functionality and uses clear, direct language without unnecessary elaboration, making it easy to parse quickly.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the complexity of file processing and aggregation, no annotations, and no output schema, the description is incomplete. It doesn't explain the return values, error conditions, or behavioral traits like whether it modifies the original file. For a tool with 4 required parameters and computational operations, more context is needed.

  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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by implying the relationship between group_column and agg_column, but doesn't provide additional syntax, format details, or examples. Baseline 3 is appropriate when the schema does the heavy lifting.

  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 tool's purpose with specific verbs ('group rows' and 'compute an aggregate') and identifies the resource (CSV rows). It distinguishes from siblings like csv_aggregate or csv_filter by focusing on grouping with aggregation, but doesn't explicitly differentiate from csv_aggregate which might have similar functionality.

  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 provided on when to use this tool versus alternatives like csv_aggregate or csv_filter. The description implies usage for grouping and aggregation tasks but offers no explicit context, prerequisites, or exclusions for selecting this tool over siblings.

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

• csv_filterB
  3.1/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the return format (JSON), it doesn't address important behavioral aspects like error handling (e.g., what happens if the file doesn't exist or column isn't found), performance characteristics, memory usage with large files, or whether the operation modifies the original CSV file. The description provides minimal behavioral context beyond the basic 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 at just two sentences that directly state the core functionality and output format. Every word earns its place with zero redundancy or fluff. The information is front-loaded with the primary purpose stated immediately.

  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 tool with 7 parameters, no annotations, and no output schema, the description is minimally adequate. It covers the basic operation and output format but lacks important context about error conditions, performance, and how this tool differs from sibling CSV tools. The absence of output schema means the description should ideally say more about the return structure, but it only mentions the format (JSON), not the content.

  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?

  With 100% schema description coverage, the input schema already documents all 7 parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how sort_by interacts with filtering) or provide usage examples. The baseline score of 3 reflects adequate but not enhanced parameter documentation.

  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 tool's purpose: 'Filter CSV rows by a column condition' specifies the verb (filter) and resource (CSV rows), and 'Returns matching rows as JSON' indicates the output format. However, it doesn't explicitly differentiate from sibling tools like csv_sample or csv_unique, which may also involve row selection operations.

  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?

  The description provides no guidance on when to use this tool versus alternatives. With siblings like csv_aggregate, csv_group_by, and csv_sample available, there's no indication of when filtering by column condition is preferred over other CSV manipulation approaches. The description lacks any context about use cases or prerequisites.

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

• csv_sampleB
  3.1/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but doesn't describe how it behaves—for example, whether it reads the entire file into memory, handles large files efficiently, or returns structured data. This leaves significant gaps in understanding the tool's operational characteristics.

  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 directly states the tool's purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.

  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 moderate complexity (reading and sampling CSV data) and the absence of annotations and output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, error handling, or output format, which are important for a data sampling tool.

  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 input schema has 100% description coverage, clearly documenting all three parameters (file_path, count, offset) with their types, defaults, and meanings. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score for high schema coverage.

  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 ('Get a sample of rows') and resource ('from a CSV file'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings like csv_describe or csv_filter, which might also involve CSV data operations, so it doesn't reach the highest score.

  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?

  The description provides no guidance on when to use this tool versus alternatives like csv_describe or csv_filter. It lacks context about specific use cases, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.

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

• csv_uniqueB
  3.1/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. While it describes the basic operation (getting unique values with counts), it doesn't mention important behavioral aspects: whether this is a read-only operation, what happens with large datasets, how missing values are handled, or what the output format looks like. For a data processing tool with zero annotation coverage, this represents significant gaps in behavioral transparency.

  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 - a single sentence that directly states the tool's purpose. There's zero wasted language, and the information is front-loaded with the core functionality. Every word earns its place in this minimal but complete statement of what the tool does.

  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 moderate complexity (data processing with 3 parameters) and 100% schema coverage but no annotations or output schema, the description is minimally adequate. It states what the tool does but doesn't provide context about output format, error conditions, or performance characteristics. For a tool that processes CSV files and returns statistical information, more context about the return structure would be helpful since there's no output schema.

  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 schema description coverage is 100%, with all three parameters well-documented in the input schema. The description doesn't add any parameter semantics beyond what's already in the schema - it doesn't explain the relationship between parameters or provide usage examples. With complete schema coverage, the baseline score of 3 is appropriate since the schema does the heavy lifting for parameter documentation.

  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 tool's purpose: 'Get unique values in a column with their counts' - a specific verb ('Get') and resource ('unique values in a column'). It distinguishes from siblings like csv_aggregate or csv_group_by by focusing on unique value extraction rather than aggregation or grouping operations. However, it doesn't explicitly mention CSV file processing, though this is implied by the tool name and context.

  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?

  The description provides no guidance on when to use this tool versus alternatives. With siblings like csv_aggregate, csv_filter, and csv_group_by available, there's no indication of when csv_unique is appropriate versus when other tools might be better suited for similar data analysis tasks. The description lacks any 'when-to-use' or 'when-not-to-use' context.

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

• csv_aggregateB
  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 carries the full burden of behavioral disclosure. It mentions the operation types but doesn't cover critical behaviors like error handling (e.g., for non-numeric columns), performance considerations (e.g., for large files), or output format (since no output schema exists). This leaves significant gaps for an agent to understand how the tool behaves beyond basic functionality.

  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 core purpose without unnecessary words. It directly states the action and key details (aggregate types and target), making it highly concise and well-structured for quick comprehension.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness2/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's complexity (aggregation operations on CSV data) and lack of annotations and output schema, the description is incomplete. It doesn't explain the return values (e.g., numeric result or error messages), handling of edge cases, or dependencies like file format requirements. For a tool with no structured behavioral data, this leaves the agent under-informed about critical operational aspects.

  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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value by listing the aggregation operations, which are already in the enum in the schema. It doesn't provide additional context like examples or constraints beyond what's in the schema, meeting the baseline for high coverage but not enhancing understanding.

  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 'compute' and the resource 'aggregate on a numeric column', with specific operations listed. It distinguishes from siblings like csv_describe or csv_filter by focusing on aggregation rather than description or filtering. However, it doesn't explicitly contrast with csv_group_by, which might also involve aggregation, making it slightly less specific.

  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 when needing to compute aggregates on numeric columns in CSV files, but doesn't explicitly state when to use this tool versus alternatives like csv_group_by (which might handle grouped aggregations) or csv_sample (which might provide summaries). No exclusions or prerequisites are mentioned, leaving usage context somewhat inferred rather than clearly defined.

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

• csv_describeB
  3.3/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions loading a CSV file and returning statistics, it lacks details on error handling (e.g., invalid file paths or malformed CSV), performance characteristics (e.g., memory usage with large files), or output format specifics. This is a significant gap for a tool with no annotation coverage.

  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 core functionality. Every word earns its place, with no redundant or vague phrasing, making it easy to parse quickly.

  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 moderate complexity (single parameter, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose but lacks behavioral details and usage context. Without annotations or output schema, more guidance on errors, performance, and output structure would improve completeness.

  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 input schema has 100% description coverage, with the single parameter 'file_path' clearly documented as an absolute path. The description adds no additional parameter semantics beyond what the schema provides, such as file format expectations or encoding details. Baseline 3 is appropriate when the schema does the heavy lifting.

  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 specific action ('Load a CSV file') and the precise outputs ('return its schema, row count, and column statistics'). It distinguishes itself from sibling tools like csv_aggregate, csv_filter, etc., which perform different operations on CSV data rather than providing descriptive statistics.

  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?

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention scenarios where csv_describe is preferred over other siblings (e.g., for initial data exploration vs. transformation), nor does it specify prerequisites or exclusions for usage.

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