Server Quality Checklist

Tool Scores

• describe_tableB
  3.3/5.0
  Behavior2/5

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

  No annotations are provided, and the description only states the purpose. It does not disclose behavioral traits beyond being a read operation, such as error handling or required permissions.

  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, no wasted words. The purpose and a key usage hint are front-loaded.

  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?

  The tool has only one parameter and an output schema, reducing the need for extensive description. However, without annotations, the description lacks safety and error context, making it minimally 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?

  The schema coverage is 0%, so the description must compensate. It adds a formatting hint for table_name, which adds meaning beyond the schema's title, but does not provide full semantics.

  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 states it describes a table's schema, which is a specific verb+resource. It does not explicitly differentiate from sibling tools like glue_table, but the purpose is clear.

  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 gives a formatting hint for the parameter ('Use 'database.table' format for Glue'), but does not provide guidance on when to use this tool versus siblings or when not to use it.

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

• list_tablesA
  3.6/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It states the main action and filtering option but does not disclose any behavioral traits such as permissions, performance implications, or edge cases (e.g., empty result). It is minimally adequate for a simple listing 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?

  The description is extremely concise with a single sentence that front-loads the main action. Every word earns its place, with no superfluous text.

  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 simplicity (1 optional param, output schema exists), the description is fairly complete. It covers the core functionality and filtering. However, it could clarify the scope of 'tables' (e.g., database tables versus other entities) to enhance 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 schema coverage is 0%, so the description must add meaning. It explains that the 'database' parameter is optional and filters results, which adds value beyond the schema. However, it does not specify the expected format or behavior when the parameter is empty.

  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 available tables') and resource, and mentions optional filtering. It is distinct from sibling tools like describe_table and query_lakehouse.

  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 on when to use this tool versus alternatives like describe_table or query_lakehouse. Implicit usage context is minimal.

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

• query_lakehouseA
  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 carries the full burden of behavioral disclosure. It only states 'Execute a SQL query' without detailing important traits like whether it is read-only, destructive, authentication requirements, rate limits, or error handling.

  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, with two sentences that front-load the purpose and provide actionable tips. No unnecessary words.

  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 a single parameter and an output schema (so return format need not be described), the description is fairly complete. It covers the main action and usage hints, but could add minor details about potential errors or timeouts.

  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 one parameter (sql) with no description, but the tool description adds meaning by specifying it should be a SQL query and suggests using certain functions for different data sources. This compensates for the lack of schema 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 executes a SQL query, which is the primary action. It differentiates from siblings by suggesting a workflow involving glue_table for Glue tables, but could be more explicit about what distinguishes this tool from similar ones.

  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 usage guidance: use read_parquet() or iceberg_scan() for S3 paths, or use glue_table first for Glue tables. This gives context on how to use the tool effectively, though it does not explicitly state when not to use it or list alternatives.

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

• glue_tableA
  4.4/5.0
  Behavior4/5

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

  No annotations are provided, so description carries full burden. It discloses that the tool resolves actual data files from Iceberg manifest and handles non-standard data locations. It also indicates the side effect of creating a view. No contradictions.

  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 six lines, each adding unique value: main action, behavioral details, usage syntax, and post-call query example. No redundant or filler content.

  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 complexity of registering a view and the presence of an output schema (though not shown), the description covers the essential behavioral and usage aspects. It could mention what the tool returns, but output schema likely fills that gap.

  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?

  With 0% schema description coverage, the description compensates by specifying the parameter format 'database.table_name'. The input schema only has a title 'Table Name', so description adds significant meaning.

  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 registers a Glue Iceberg table as a queryable DuckDB view, with specific verb 'Register' and resource 'Glue Iceberg table'. It also explains it resolves data files from Iceberg manifest, distinguishing it from siblings like describe_table or list_tables.

  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 explicit usage syntax 'glue_table('database.table_name')' and post-call query example 'SELECT * FROM database__table_name'. It implicitly guides when to use this tool, but does not explicitly state when not to use or mention alternatives.

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