Server Quality Checklist

Tool Scores

• apple_podcasts_followersA
  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 the full burden. It discloses a key behavioral nuance: 'Read the resolvedColumn field in the response before interpreting the numbers: Apple's follower column has been both a running total and a per-period count, and which one you get changes what 'change' means.' It also mentions output structure (one value per period plus change) and implicitly that it's a read 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 three tightly written sentences. The first sentence states purpose and output, the second gives a use case, and the third warns about interpreting the data. No wasted words; front-loaded and easy to parse.

  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?

  There is no output schema, so the description must compensate. It explains the data shape (one value per period plus change) and the critical resolvedColumn caveat. It could be more explicit about the response structure, but for a trend tool this is sufficient. The schema fills in parameter behavior, and the description covers the essential interpretation pitfall.

  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 provides 100% coverage with detailed descriptions for all four parameters, including defaults, date formats, and truncation behavior. The description adds no parameter-specific guidance, but the schema already does the heavy lifting, so a baseline of 3 is appropriate.

  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 function: 'Follower trend for the show on Apple Podcasts over a date range: one value per period plus the change across the window.' It specifies the resource (follower trend) and scope (over a date range), distinguishing it from sibling tools like apple_podcasts_summary and apple_podcasts_episodes.

  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 an explicit use case: "Use it for 'is my Apple following growing'." This tells the agent when to choose this tool. However, it does not name alternative tools or provide exclusion criteria, so it misses the 'when-not-to-use' aspect.

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

• apple_podcasts_episodesA
  4.4/5.0
  Behavior5/5

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

  With no annotations provided, the description carries the full burden and excels: it discloses that Apple aggregates by unique devices (device counts not headcounts), explains the engaged listener threshold (20 minutes or 40%), and notes the bounded response requirement. This is rich behavioral context beyond the schema.

  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 concise—four sentences, each earning its place: purpose, use case, metric semantics, and response-boundary constraint. It is front-loaded and contains no fluff or repetition.

  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?

  The rich schema combined with the description covers purpose, metrics semantics, and constraints well. There is no output schema, but the description implies output structure (ranked episodes). Minor ambiguity about 'required' params (schema shows defaults) leaves a small gap, preventing a 5.

  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%, with all 7 parameters well-documented. The description adds rationale for date range and limit (bounded response) but does not substantially enhance per-parameter meaning beyond what the schema already provides, so the baseline of 3 is appropriate.

  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 provides per-episode listening metrics (plays, unique listeners, engaged listeners) over a date range, rolled up and ranked. It uses specific verb and resource (Apple Podcasts Connect episodes) and is well differentiated from siblings like summary and followers by focusing on episode-level attention.

  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 context on when to use this tool ('Answers which episodes held attention on Apple') and implies differentiation from siblings (summary, followers). However, it does not explicitly name alternatives or state exclusions, so it stops short of full guidance.

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

• apple_podcasts_summaryA
  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 carries the full burden and does so excellently. It discloses that Apple counts unique devices rather than people, defines an engaged listener precisely (20 minutes or 40% of an episode), and warns that each period is a separate API call to Apple with throttling implications. These are non-obvious behavioral facts essential for correct use.

  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?

  Four tight sentences: state what it does, define key metrics, give usage context, and warn about API behavior. No filler or repetition; every sentence earns its place and the most important information is front-loaded.

  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 having no output schema, the description tells the caller what to expect in return (metrics per period plus totals) and gives necessary caveats (device-based counting, engaged listener threshold, Apple's lag, range limits). For a read-only reporting tool with a fully specified input schema, this is complete enough to invoke 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?

  The input schema already covers 100% of parameters with detailed descriptions, so the baseline is 3. The description adds value by explaining why 'keep the range short' (each period is a separate call) and by defining what the returned listener metrics actually mean, which clarifies the data behind the parameters. It doesn't describe each parameter individually, but the schema already does 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 opens with 'Show-level listening summary from Apple Podcasts Connect' and enumerates exactly which metrics are returned (plays, unique listeners, engaged listeners, followers) and how they are aggregated (per period and totalled). This clearly distinguishes it from siblings like apple_podcasts_episodes and apple_podcasts_followers by scope and content.

  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 a direct usage cue: 'Use it for "how is the show doing on Apple" questions.' It also notes that this is owner-side data not exposed by hosting APIs. However, it does not explicitly mention when NOT to use it or name alternative sibling tools, so it falls short of the top tier.

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

• apple_podcasts_check_accessA
  4.9/5.0
  Behavior5/5

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

  With no annotations, the description fully discloses behavioral traits: returns no listening data, and provides critical context about token expiry (180 days) and how failures manifest. This goes beyond basic description to help agents diagnose issues.

  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 the core action, followed by usage guidance and return behavior. Every sentence provides value without redundancy. Efficient and well-structured.

  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?

  For a simple check tool with no output schema, the description covers purpose, usage timing, return contents, and an important expiration caveat. It is self-contained and sufficient for an agent to invoke 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?

  No parameters exist, so schema coverage is trivially 100%. The description adds no param details because there are none. Baseline for 0 params is 4, and the description appropriately clarifies the tool takes no input.

  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: verify credentials and list vendor numbers. It uses specific verbs ('verify', 'list') and identifies the resource (Apple Podcasts Connect credentials, access token). This distinguishes it from sibling tools that likely return data summaries or episode lists.

  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 instructs when to run: 'Run this first, and run it whenever another tool reports an auth failure'. This provides a clear trigger condition and implies it should precede other tools. Though it doesn't name alternatives, the context of auth failure makes the choice unambiguous.

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