Server Quality Checklist

Tool Scores

• field_discoveryB
  3.1/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden; it only states a read-like action but fails to disclose behavioral aspects such as permissions, rate limits, or response behavior, and doesn't clarify what happens without a source.

  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 with no redundant words, front-loading the action and resource effectively.

  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 that there are no annotations, no output schema, and sparse schema descriptions, the description is incomplete: it lacks parameter explanations (especially 'search'), usage context, and behavioral details, making it insufficient for reliable tool selection.

  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%, and the description only indirectly explains 'source' (as the marketing data source); the 'search' parameter is completely undocumented in both schema and description.

  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 'List' and resource 'available fields' with scope 'for a given marketing data source,' clearly distinguishing from sibling tools like data_source_discovery and accounts_discovery.

  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; the description merely implies usage for listing fields, without specifying prerequisites or comparing to data_source_discovery/accounts_discovery.

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

• data_queryB
  3.2/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden of disclosing behavior. It only states that the tool 'executes' a query, with no mention of whether the query is read-only, what the response format is, whether results are returned directly or via an ID, or any side effects. This is a significant transparency gap for a query execution tool.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is a single, front-loaded sentence that efficiently communicates the core action. There is no fluff, and the key inputs are listed early. However, it is so brief that it sacrifices informative value, but this dimension specifically rewards economy and clarity, which this achieves.

  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 (5 parameters, nested objects, no output schema), the description is incomplete. It does not explain how the parameters interrelate, what the returned data looks like, or how the tool fits into the overall query workflow beyond the implied 'selected' inputs. The sibling tool names suggest a workflow but are not sufficient documentation for effective use.

  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 coverage is 0%, so the description must compensate. It mentions source, accounts, fields, and date range, giving a high-level hint of their roles, but omits the filters parameter entirely. It doesn't describe expected formats, structures, or constraints for any parameter, leaving the agent to guess.

  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 identifies the tool as executing a marketing analytics query with specific inputs (source, accounts, fields, date range). This verb-resource pairing is specific and distinguishes it from sibling discovery tools (data_source_discovery, accounts_discovery, field_discovery) and the results retrieval tool (get_query_results), as this is the step that actually runs the query.

  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 a workflow: use this after selecting source, accounts, and fields (likely via the discovery tools) and to execute a query. However, it does not explicitly state when to use this tool versus get_query_results or provide exclusions. The context is clear but not explicit enough for a higher score.

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

• accounts_discoveryA
  3.5/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the burden of behavioral disclosure. It communicates that this is a read-only listing operation ('List'), which is useful. However, it does not disclose the return format, pagination behavior, or potential limitations, leaving some behavioral aspects opaque.

  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 front-loads the core action ('List connected marketing accounts') and context. Every word adds meaning, and there is no redundant or extraneous content.

  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 absence of an output schema and annotations, the description needs to explain return values and parameter behavior. It does neither, leaving the 'source' parameter underspecified and the response format unknown. The tool is simple, but the lack of parameter and output detail makes it incomplete for an agent to invoke reliably.

  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 0% description coverage for the 'source' parameter, and the description only vaguely references 'the selected data source' without explaining what the parameter should contain or how to use it. This is insufficient for a parameter that is an object, leaving the agent guessing about its structure.

  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 'List' and a clear resource 'connected marketing accounts', with a context 'selected data source'. This clearly distinguishes it from sibling tools like data_source_discovery and field_discovery, which target different resource types.

  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 phrase 'for the selected data source' implies the tool is used after a data source is selected, providing some usage context. However, it does not explicitly state when to prefer this over alternatives or mention any exclusions, so guidance is only implicit.

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

• data_source_discoveryA
  3.6/5.0
  Behavior3/5

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

  No annotations are present, so the description carries the burden. The verb 'List' implies a read-only operation, and 'configuration requirements' provides some behavioral context. However, it doesn't disclose authentication needs, pagination, or any 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 a single concise sentence that clearly states the tool's purpose. It is front-loaded and contains no redundant information.

  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 is simple, but the description leaves the 'search' parameter unexplained and gives no usage guidance relative to siblings. Given the simple schema and lack of output schema, some gaps are acceptable, but the param gap makes it incomplete.

  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 optional 'search' object with no description, and the description does not mention this parameter at all. At 0% schema description coverage, the description fails to compensate, leaving the parameter's purpose and format unclear.

  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 lists available marketing data sources along with their configuration requirements. It distinguishes from sibling tools like accounts_discovery and field_discovery by focusing specifically on data sources.

  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 use when you need to see data sources, but it doesn't explicitly mention alternatives or when not to use this tool. No guidance is provided about how it relates to other discovery tools.

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

• get_query_resultsA
  3.8/5.0
  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. 'Retrieve' implies a read operation, but it does not disclose whether the result is immediately available, whether it can be fetched multiple times, or any error conditions (e.g., invalid schedule ID). For a retrieval tool, more behavioral context would be valuable.

  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 sentence that is front-loaded and contains no extraneous words. It efficiently communicates the tool's core function.

  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 tool with one parameter and no output schema, the description adequately conveys the purpose and parameter semantics. It could be improved by mentioning the return format or potential delays, but the current level of detail is sufficient for basic invocation.

  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%, and the only parameter 'scheduleId' is just a string in the schema. The description adds meaning by stating it is the ID of a previously executed query, which directly ties the parameter to the tool's purpose.

  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 ('Retrieve'), the resource ('the result of a previously executed query'), and the key identifier ('by schedule ID'). This distinguishes it from sibling tools like data_query, which likely executes a query, and discovery tools, which explore metadata.

  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 phrase 'previously executed query' implies this tool is used after a query has been run, but it does not explicitly state when to use this vs. alternatives like data_query. No direct alternative or exclusion is mentioned.

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