Server Quality Checklist

Tool Scores

• make_api_requestC
  2.5/5.0
  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. It mentions getting types first but doesn't disclose critical behavioral traits like authentication needs, rate limits, error handling, or what the tool returns. For a general API tool with no annotations, this is a significant gap in transparency.

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

  Conciseness3/5

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

  The description is moderately concise with two sentences, but the long list of services feels cluttered and could be structured better. It's front-loaded with the main purpose, but the list doesn't earn its place efficiently, reducing clarity.

  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 of a unified API tool with no annotations, no output schema, and nested objects, the description is incomplete. It lacks details on return values, error cases, and operational constraints, making it inadequate for such a broad-scope tool.

  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%, so the schema already documents the three parameters (service, method, request). The description lists available services, adding some context beyond the schema, but doesn't explain parameter interactions or provide deeper semantics. Baseline 3 is appropriate as the schema does the heavy lifting.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose3/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description states this is a 'unified tool for all Square API operations' which provides a general purpose, but it's vague about what specific actions it performs. It lists available services but doesn't specify the verb (make requests) or distinguish it from sibling tools like get_service_info or get_type_info beyond being the main API caller.

  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?

  The description includes 'Be sure to get types before calling' which implies a prerequisite, but it doesn't explain when to use this tool versus alternatives or provide explicit guidance on context. No exclusions or clear alternatives are mentioned, leaving usage unclear.

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

• get_service_infoA
  3.9/5.0
  Behavior3/5

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

  With no annotations provided, the description carries full burden. It states this is a 'Get' operation (implying read-only) and establishes a prerequisite relationship with 'get_type_info'. However, it doesn't disclose other behavioral traits like authentication requirements, rate limits, error conditions, or what format the information returns. The description adds some context but leaves significant behavioral aspects unspecified.

  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 just two sentences. The first sentence states the core purpose, and the second provides crucial usage guidance. Every word earns its place with zero wasted text, making it front-loaded and efficient for an AI agent to parse.

  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?

  Given the tool has one parameter with full schema coverage but no annotations and no output schema, the description provides adequate purpose and usage guidance. However, it doesn't explain what information is returned or address behavioral aspects like error handling. For a simple read operation, it's minimally complete but lacks details about the return format that would be helpful without an output schema.

  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%, so the schema already documents the single 'service' parameter with its type and description. The description adds no additional parameter semantics beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description.

  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 verb 'Get' and resource 'information about a Square API service', making the purpose evident. It distinguishes itself from 'get_type_info' by focusing on service-level information rather than type details. However, it doesn't specify what kind of information is retrieved (e.g., capabilities, endpoints, status), keeping it from a perfect score.

  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?

  The description explicitly provides usage guidance: 'Call me before trying to get type info', indicating this tool should be used as a prerequisite for the sibling tool 'get_type_info'. This creates a clear workflow relationship and distinguishes it from 'make_api_request' by focusing on metadata rather than actual API calls.

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

• get_type_infoA
  4.1/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool's prerequisite nature, which is valuable context, but doesn't describe what 'type information' includes, potential error conditions, or response format. For a tool with no annotations, this leaves significant behavioral gaps.

  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 just two sentences, both of which earn their place by stating the purpose and critical usage guideline. It's front-loaded with the core function and wastes no words, making it highly efficient for agent comprehension.

  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?

  Given the tool's complexity (prerequisite for API calls), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'type information' entails, how it's used with make_api_request, or what the return values look like. While it covers purpose and usage well, it leaves too many contextual gaps for a tool in this role.

  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%, so the schema already fully documents both parameters. The description doesn't add any parameter-specific details beyond what the schema provides, such as examples of valid service/method combinations or constraints. This meets the baseline for high schema coverage.

  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 specific action ('Get type information') and resource ('for a Square API method'), and distinguishes it from sibling tools by explicitly mentioning its prerequisite role before calling make_api_request. It uses precise language that leaves no ambiguity about its function.

  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?

  The description provides explicit usage guidance by stating 'You must call this before calling the make_api_request tool,' which clearly defines when to use this tool versus its sibling. It establishes a clear workflow dependency, making it easy for an agent to understand its contextual role.

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