Server Quality Checklist

Tool Scores

• check_healthA
  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. It discloses what the tool reports (freshness, row-count movement, null rates, dbt test results, one-line verdict) and how to act on the verdict, which goes beyond a vague 'checks health'. It does not explicitly state read-only behavior or side effects, but the nature of the tool implies a safe inspection. This is solid but not exhaustive.

  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 clear usage guidance, a concise list of reported metrics, interpretation instructions, and the parameter description. Every sentence adds value and there is no redundancy or filler.

  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 has a single parameter and an output schema, the description provides ample context: what the tool does, when to use it, what it returns (including specific metrics and verdict), and how to interpret results. It is fully self-contained and leaves no critical gaps for an agent to operate correctly.

  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?

  Schema coverage is 0%, but the description compensates with an Args section: 'table: Table name, fully qualified or bare.' This adds format guidance beyond the bare type of 'string' in the schema, giving the agent enough to construct a valid call.

  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 begins with a specific verb+resource: 'Check whether a table is safe to rely on right now.' This clearly distinguishes the tool from siblings like search_tables, describe_table, and trace_lineage, which address different concerns (discovery, structure, lineage).

  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 explicit when-to-use guidance: 'Call this before presenting any number derived from a table you have not already checked, and whenever a user mentions a table you did not choose yourself.' It also explains how to interpret verdicts. However, it does not explicitly state when not to use the tool or name alternatives, so it falls slightly short of a 5.

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

• describe_tableA
  4.6/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden. It discloses the interpretive behavior of the `unit` field, explaining that it distinguishes gross/net and cents/dollars, and instructs the agent to say 'unknown' rather than guess when the unit is undocumented. This adds valuable context beyond the schema, but it doesn't cover other potential behaviors like error handling or access requirements.

  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 purpose, followed by usage guidance and a clearly structured Args section. Every sentence adds information, from the unit field warning to the parameter explanations, with no filler. It's concise yet complete.

  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 covers the essential context for using this tool: when to call it, what to expect (purpose, grain, owner, status, columns), and the key interpretation caveat about the unit field. Since an output schema is present, it doesn't need to enumerate return values. It's complete for the tool's complexity.

  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?

  The schema has no descriptions for the parameters (0% coverage), but the description compensates fully with an Args section. For `table`, it provides an example of a fully qualified name; for `include_columns`, it explains when to set it to False. This gives the agent the needed semantic detail.

  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 starts with a clear, specific statement: 'Get the full meaning of one table: purpose, grain, owner, status, and columns.' This immediately identifies the verb, resource, and scope, distinguishing it from sibling tools like search_tables, which searches across tables, and trace_lineage, which follows relationships.

  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 a clear usage rule: 'Call this before using any table you have not already described in this conversation.' It also gives conditional guidance for include_columns, saying to set False when only purpose, owner, and status are needed. However, it doesn't explicitly mention alternative tools or describe when not to use it beyond the already-described condition, so it lacks the full 5-level criteria.

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

• trace_lineageA
  4.6/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full burden. It discloses a key behavioral nuance: 'Edges marked model_only mean the SQL could not be parsed down to the column — the dependency is real, the precision is not.' This goes beyond the basic operation and warns about data-quality limitations.

  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 compact, front-loaded with the primary purpose, then uses a short usage-rule sentence and a tight Args list. No filler; every sentence and parameter comment serves a purpose.

  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?

  Despite lack of annotations, the description covers purpose, usage triggers, parameter semantics, and a precision caveat. Since an output schema exists, the absence of return-format details is acceptable. The tool is fully specified for an agent to select and invoke 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?

  The schema has 0% description coverage, but the Args section explains every parameter: table as 'fully qualified or bare', column as optional for table-level lineage, direction with valid values, and depth with a hop range. This adds meaning beyond the bare schema names and defaults.

  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 a specific verb and resource: 'Trace where a table or column's data comes from, and what depends on it.' This clearly defines lineage tracing and distinguishes it from sibling tools like search_tables, describe_table, and check_health.

  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 explicit trigger scenarios: 'when you need to justify a number, when a value looks wrong and you need to find where it was computed, or before suggesting a change to a table so you can say what it would break.' It does not name alternatives directly (e.g., use describe_table for schema-only questions), but the context is clear enough.

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

• search_tablesA
  4.8/5.0
  Behavior5/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It goes beyond a simple 'search' by revealing that results include deprecated tables (labeled), are ranked by relevance, and include owner and status. This helps the agent understand the return value and potential pitfalls, exceeding the minimum expected for a read-only search 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 appropriately sized and well-structured: it opens with the purpose, then provides usage context, then describes return behavior, and finally lists arguments with examples. Every sentence adds value, with no redundancy or filler.

  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 has only two simple parameters and no annotations, the description covers all essentials: when to call it, what it returns, and how parameters behave. The existence of an output schema means the description need not detail the return format, but it still mentions key output traits. It is complete for the tool's complexity.

  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%, so the description must compensate. It fully explains both parameters: 'query' is described as 'What you are looking for, e.g. ...' and 'limit' is described as 'Maximum candidates to return (1-25).' This adds meaningful constraints and examples that are absent from the input 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 starts with a clear, specific verb phrase: 'Find warehouse tables by describing what you want in plain English.' It identifies the resource (warehouse tables) and the method (natural language query), distinguishing it from sibling tools like describe_table or trace_lineage. The purpose is immediately obvious.

  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 explicit guidance on when to use the tool: 'Call this before writing SQL whenever you are not certain which table holds the data.' It explains why table names alone are insufficient, but it does not explicitly name alternatives or state when not to use it. Clear context is provided, but exclusion/alternative guidance is missing.

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