Server Quality Checklist

Tool Scores

• get_post_performanceA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden; it discloses pagination behavior (page, all_pages, max_pages), specifies that returns are 'API response verbatim', and clarifies parameter constraints like max profile_ids and datetime format.

  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 well-structured with a summary line and organized Args section; it is slightly verbose but every sentence adds value.

  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 10 parameters and the presence of an output schema, the description adequately covers return format ('API response verbatim') and usage context; minor gaps in edge-case behavior (e.g., empty results) prevent a perfect score.

  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 coverage is 0%, but the description fully compensates by detailing every parameter with examples, defaults, constraints, and usage notes (e.g., lifetime prefix for metrics, timezone format).

  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 'Pull post-level analytics for posts created in a datetime range', a specific verb-resource combination that distinguishes it from sibling tools like list_profiles and get_profile_metrics.

  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 provides context for when to use (e.g., for per-post analysis and pagination control) but does not explicitly mention alternatives or conditions to avoid using this tool.

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

• list_profilesA
  4.5/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries full burden. It discloses that the tool returns a list of profiles and describes the fields included (customer_profile_id, network_type, etc.), which is sufficient for a simple read-only list 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: two sentences. The first sentence front-loads the purpose, and the second adds essential context. No redundant or unnecessary information.

  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 no parameters, the presence of an output schema, and the tool's simplicity, the description is complete. It explains what the tool does, what it returns, and how it fits into the workflow (prerequisite for analytics calls).

  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?

  There are no parameters, so schema coverage is 100%. The description adds meaning beyond the schema by explaining the output fields and the context of usage. Baseline for 0 params is 4, and this description meets that.

  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 verb 'list' and the resource 'social profiles connected to this Sprout customer.' It also distinguishes itself from siblings by noting that this is the only source of customer_profile_id, which is essential for analytics calls.

  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 guidance by saying 'Start here' and explaining that every analytics call is scoped by customer_profile_id, implying this tool is a prerequisite. However, it does not explicitly state when not to use it or compare to alternatives.

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

• list_metricsA
  4.7/5.0
  Behavior5/5

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

  With no annotations provided, the description fully discloses behavior: it returns names, API keys, premium requirements, and notes that calculated metrics are NOT returned by the API. It also warns to check live docs if a key is rejected, demonstrating thorough transparency.

  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 well-structured with clear paragraphs and front-loaded purpose. Each sentence adds value, though it could be slightly more concise without losing clarity.

  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 output schema exists, the description still adds value by explaining the catalog contents and calculated metrics. It covers parameters, return structure, and provides a fallback reference to live docs, making it complete for the agent.

  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 coverage is 0%, so the description fully compensates by listing exact allowed values for network (all social platforms) and scope (profile, post, all) with explanations of their meanings, adding crucial context beyond the schema's type 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 clearly states the tool's purpose: 'Look up valid Sprout metric keys for a network, so calls don't guess.' It uses a specific verb and resource, and distinguishes itself from sibling tools like get_profile_metrics and get_post_performance, which fetch actual metrics.

  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 clear usage context: specify a network or omit for all, and choose scope for the appropriate endpoint. It implies use before making metric calls, but does not explicitly state when not to use or provide alternative tools.

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

• get_customer_idA
  4.8/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries full burden. It correctly indicates a read operation without side effects and specifies the return fields (customer_id, name). Lacks mention of rate limits or authentication details, but for a simple list tool, it is sufficiently transparent.

  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 with front-loaded purpose. Each sentence adds value: the first states the action and resource, the second provides usage guidance and return info. No wasted words.

  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 no parameters and an output schema (though not shown), the description covers the purpose, usage context, and return fields. It is complete for a list operation tool.

  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 tool has zero parameters, and schema description coverage is trivially 100%. The description adds no redundant parameter information, which is appropriate. No additional semantics needed beyond what the schema provides.

  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 Sprout customer IDs accessible by the API token, using specific verbs ('List') and identifies the resource ('Sprout customer IDs'). It distinguishes from sibling tools (list_profiles, list_metrics, etc.) by focusing on customer ID retrieval.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly states when to use the tool: when SPROUT_CUSTOMER_ID is unknown or to confirm which account the token points at. This guides the agent on appropriate context and implies alternatives (e.g., not needed when customer ID is already known).

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

• get_profile_metricsA
  4.8/5.0
  Behavior5/5

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

  No annotations are provided, so the description carries full behavioral burden. It discloses return format (verbatim API response with dimensions and metrics), pagination behavior (all_pages, max_pages), that rates are not returned (must be derived), and network-specific metrics. This is comprehensive and transparent.

  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 well-structured: a one-line summary followed by bullet points for each parameter, a return description, and a note. Every sentence adds value, and it is front-loaded with the purpose. It is concise yet comprehensive.

  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 parameters are fully explained, an output schema exists (so return values need not be detailed), and the description includes a key note about derived rates, the description is complete for an agent to invoke this tool 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?

  Despite 0% schema description coverage, the description explains all 7 parameters in detail: types, constraints, examples, defaults, and relationships to other tools (list_profiles, list_metrics). This adds significant value beyond the schema alone.

  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's purpose: 'Pull profile-level analytics for a date range (daily granularity).' It identifies the verb and resource, and the sibling tools are distinct (e.g., list_profiles, get_post_performance), so there is no ambiguity.

  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 strong usage context, including how to obtain profile_ids from list_profiles, how to find valid metric keys via list_metrics, and constraints like max 100 IDs and date range <=1 year. It does not explicitly state when NOT to use this tool (e.g., for individual post performance, use get_post_performance), but the context of sibling tools and the detailed parameter guidance make it clear.

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