Server Quality Checklist

Tool Scores

• get_first_rowsC
  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 but only states the basic action. It doesn't mention what 'first rows' means (e.g., how many rows, ordering), whether this is a read-only operation, potential rate limits, or authentication needs beyond the optional auth_token parameter. This leaves significant gaps for an agent to understand the tool's 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 that front-loads the core purpose with zero wasted words. It directly communicates the tool's function without unnecessary elaboration, making it easy for an agent 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 lack of annotations and output schema, the description is incomplete for a tool with 4 parameters. It doesn't explain the return format (e.g., number of rows, data structure), error conditions, or how it interacts with siblings. For a dataset retrieval tool, this leaves critical context gaps for an agent to use it effectively.

  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%, so the schema fully documents all parameters (dataset, config, split, auth_token). The description adds no additional parameter semantics beyond what's in the schema, such as clarifying what 'first rows' entails in relation to these parameters. This meets the baseline 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 first rows') and resource ('from a Hugging Face dataset split'), making the purpose immediately understandable. It distinguishes this tool from siblings like 'get_rows' by specifying 'first rows' rather than arbitrary rows, though it doesn't explicitly contrast with other siblings like 'get_parquet' or 'get_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 like 'get_rows' for arbitrary rows, 'get_parquet' for file downloads, or 'get_statistics' for summaries. It mentions 'split' but doesn't explain why one might choose this tool over others for dataset exploration or initial inspection.

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

• get_parquetC
  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 full burden for behavioral disclosure. It states this is an export operation but doesn't clarify whether this creates a file download, returns data in memory, requires specific permissions, has rate limits, or what happens with large datasets. The mention of 'auth_token' in the schema hints at authentication needs, but the description doesn't elaborate on this behavioral aspect.

  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 communicates the core functionality without any wasted words. It's appropriately sized and front-loaded with the essential information.

  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?

  For a data export tool with no annotations and no output schema, the description is insufficient. It doesn't explain what format the exported data returns in, whether it's a file or structured data, how large datasets are handled, or what authentication scenarios require the optional auth_token. Given the complexity of data export 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?

  With 100% schema description coverage, the schema already fully documents both parameters. The description doesn't add any additional meaning about the parameters beyond what's in the schema, so it meets the baseline for high schema coverage without providing extra value.

  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 ('Export') and resource ('Hugging Face dataset split as Parquet file'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_rows' or 'get_first_rows' that might also retrieve dataset content, 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 'get_rows' or 'filter'. It mentions exporting as Parquet, but doesn't explain why one would choose this format over other retrieval methods available in the sibling tools.

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

• get_rowsC
  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 pagination (100 rows per page) but fails to describe critical behaviors such as rate limits, error handling for invalid inputs, whether it's read-only or has side effects, or what the output format looks like. This leaves significant gaps in understanding how the tool operates.

  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 ('Get paginated rows from a Hugging Face dataset'). It wastes no words and directly communicates the essential action, 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 complexity of a 5-parameter tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., rate limits, error handling), usage context relative to siblings, and output format, which are crucial for an AI agent to invoke this tool effectively in a dataset retrieval scenario.

  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, providing clear documentation for all 5 parameters (e.g., dataset format, config usage, split purpose, page details, auth token). The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline score of 3 without compensating or detracting.

  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 paginated rows') and resource ('from a Hugging Face dataset'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its siblings like 'get_first_rows' or 'search_dataset', which might offer similar data retrieval with different scopes or methods.

  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 'get_first_rows' (for initial rows) or 'search_dataset' (for filtered results). It mentions pagination but doesn't clarify scenarios where paginated retrieval is preferred over other methods, leaving the agent without context for tool selection.

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

• get_statisticsC
  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 full burden for behavioral disclosure but offers minimal information. It doesn't mention what types of statistics are returned, whether this is a read-only operation, potential rate limits, authentication requirements beyond the optional token parameter, or how it handles large datasets. The description merely restates the tool's basic function without behavioral context.

  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 6 words, front-loading the essential information with zero wasted language. Every word contributes directly to understanding the tool's function, making it efficient despite its brevity.

  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?

  For a tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what statistics are returned, how they're formatted, whether this is a computationally intensive operation, or how it differs from other dataset retrieval tools. The minimal description leaves too many contextual gaps for effective agent 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 description adds no parameter-specific information beyond what's already in the schema, which has 100% description coverage. All four parameters (dataset, config, split, auth_token) are fully documented in the schema with descriptions, examples, and requirements. The description doesn't provide additional context about how these parameters interact or affect the statistical output.

  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 statistics') and target resource ('about a Hugging Face dataset'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings like 'get_info' or 'get_rows', which also retrieve dataset information but serve different purposes.

  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. While the input schema mentions using 'get_info to list available configs', this is parameter-specific advice, not overall usage guidance. There's no indication of when statistical analysis is preferred over raw data retrieval from sibling tools.

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

• search_datasetC
  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 full burden for behavioral disclosure. It states what the tool does but doesn't describe important behavioral aspects: whether this is a read-only operation, what format results are returned in, whether there are rate limits, how large datasets are handled, or what happens with invalid queries. The description is minimal and lacks operational context.

  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, clear sentence that efficiently communicates the core functionality. It's front-loaded with the essential information and contains no unnecessary words or redundant information. This is an excellent example of conciseness.

  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?

  For a search tool with 5 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, how matches are returned, whether there's pagination, or any error conditions. The combination of complex functionality with minimal description creates significant gaps for an AI agent.

  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 schema already documents all 5 parameters thoroughly. The description adds no parameter information beyond what's in the schema. The baseline score of 3 reflects adequate parameter documentation through the schema alone, though the description contributes nothing additional.

  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 ('search for text') and resource ('within a Hugging Face dataset'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'filter' or 'get_rows', which might also involve dataset querying 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. While the input schema mentions using 'get_info to list available configs', this is parameter documentation rather than usage guidance. There's no indication of when search is preferred over filtering or other dataset access methods.

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

• filterB
  3.2/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'SQL-like conditions' but doesn't specify what happens (e.g., returns filtered rows, pagination details beyond schema's 'page' parameter, or performance implications). It lacks critical info like whether this is read-only, if it modifies data, rate limits, or error handling. The description adds minimal behavioral context beyond the basic action.

  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 states the tool's purpose clearly without redundancy. It's front-loaded with the core action and resource, and every word earns its place by specifying the method ('SQL-like conditions'). There's no wasted text or unnecessary elaboration.

  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 (7 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what the tool returns (filtered rows? metadata?), how results are structured, or behavioral traits like pagination beyond the schema. For a data filtering tool with multiple parameters and siblings, more context on output and usage is needed to be complete.

  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 fully documents all 7 parameters. The description adds no parameter-specific semantics beyond implying 'where' is for filtering. It doesn't explain parameter interactions, default behaviors, or provide examples not in the schema. Baseline 3 is appropriate since the schema does the heavy lifting, but the description doesn't compensate with additional insights.

  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 ('filter rows') and target resource ('Hugging Face dataset'), and specifies the method ('using SQL-like conditions'). It distinguishes from siblings like 'get_rows' or 'search_dataset' by focusing on filtering rather than retrieval or search operations. However, it doesn't explicitly contrast with all siblings, leaving some ambiguity about when to choose this over alternatives like 'search_dataset'.

  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 filtering datasets with SQL-like syntax, but provides no explicit guidance on when to use this tool versus siblings like 'search_dataset' or 'get_rows'. It mentions using 'get_info' to list configs in the schema, but this is not in the description itself. There's no mention of prerequisites, alternatives, or exclusions, leaving usage context largely implicit.

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

• validateB
  3.3/5.0
  Behavior2/5

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

  With no annotations, the description carries full burden but only states the basic check. It lacks details on behavior such as error handling, rate limits, or what 'accessible' entails (e.g., public vs. private datasets). This leaves gaps for an agent to understand operational 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place by clearly stating the tool's function.

  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 no annotations and no output schema, the description is minimal but adequate for a simple validation tool. However, it could benefit from more behavioral context (e.g., response format or error cases) to fully guide an agent, especially with siblings that might overlap in functionality.

  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 parameters are well-documented in the schema. The description adds no additional meaning beyond implying the 'auth_token' is for private/gated datasets, which is already covered in the schema. Baseline 3 is appropriate as 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 ('check if exists and is accessible') and the resource ('Hugging Face dataset'), distinguishing it from siblings like 'get_info' or 'search_dataset' which likely retrieve data rather than validate accessibility.

  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 explicit guidance on when to use this tool versus alternatives is provided. The description implies validation, but it doesn't specify scenarios like pre-download checks or differentiate from 'get_info' which might also confirm existence.

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

• get_infoA
  3.9/5.0
  Behavior3/5

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

  With no annotations provided, the description carries full burden. It discloses that the tool retrieves detailed dataset information and mentions accessibility checking via validate, but lacks behavioral details like rate limits, error handling, or response format. It does not contradict annotations.

  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?

  Two sentences, front-loaded with core purpose and followed by usage guidance. Every sentence adds value without redundancy, making it efficiently structured and appropriately sized.

  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 no annotations, no output schema, and 2 parameters with full schema coverage, the description is adequate but has gaps. It explains the tool's purpose and a prerequisite but lacks details on behavioral traits (e.g., response structure, error cases) that would enhance completeness for a read operation.

  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 fully documents both parameters (dataset identifier format and optional auth token). The description adds no additional parameter semantics beyond what the schema provides, meeting the baseline for high 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 specific action ('Get detailed information') and resource ('Hugging Face dataset'), listing key information types (description, features, splits, statistics). It distinguishes from siblings like get_statistics (subset) and validate (existence check).

  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?

  Provides explicit guidance to 'Run validate first to check if the dataset exists and is accessible,' establishing a clear prerequisite. However, it does not specify when to use this tool versus alternatives like get_statistics or get_rows, missing sibling differentiation.

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