Server Quality Checklist

Tool Scores

• check_email_reputationA
  4.2/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by disclosing behavioral traits: it explains the comprehensive analysis scope, mentions API dependencies (Abstract API), and includes error handling details in the 'Raises' section. However, it doesn't mention rate limits, authentication requirements beyond the API key error, or whether this is a read-only operation.

  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 appropriately front-loaded with purpose and usage, but becomes overly verbose with an extremely detailed example (60+ lines) that duplicates information already implied by the return structure description. The 'Raises' section is useful but could be more concise.

  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's complexity (comprehensive reputation analysis), no annotations, and no output schema, the description provides exceptional completeness: detailed purpose, parameter semantics, comprehensive return structure documentation, example output, and error handling. Nothing essential is missing for agent understanding.

  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?

  With 0% schema description coverage and only one parameter, the description compensates fully by providing detailed semantics for the 'email' parameter in the Args section, explaining it's 'The email address to analyze for reputation' with clear type information and usage context.

  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 'analyzes email reputation using Abstract API's Email Reputation service' with specific verbs ('analyzes', 'provides comprehensive analysis') and distinguishes it from sibling tools (validate_phone, verify_email) by focusing on reputation analysis rather than validation or verification.

  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 usage context ('designed to help improve delivery rates, clean email lists, and block fraudulent users') but doesn't explicitly state when to use this tool versus the sibling tools (validate_phone, verify_email). No explicit alternatives or exclusions are provided.

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

• verify_emailA
  4.3/5.0
  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 of behavioral disclosure. It effectively describes the tool's behavior: it uses an external API, returns detailed validation results, and includes error handling (raises exceptions for missing API key, HTTP errors, or other issues). It covers key aspects like what the tool does and potential failures, though it could add more on rate limits or performance.

  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 sections (purpose, args, returns, example, raises) and front-loaded key information. However, it includes an extensive example and detailed return value breakdown that might be verbose; some of this could be streamlined without losing clarity, but overall it remains efficient and informative.

  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's complexity (external API integration, detailed output) and no annotations or output schema, the description is highly complete. It covers purpose, parameters, return values with examples, and error handling, providing all necessary context for an AI agent to understand and use the tool effectively without relying on structured fields.

  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 provides detailed parameter semantics: 'email (str): The email address to validate.' This adds clear meaning beyond the bare schema, explaining the parameter's purpose and type, which is essential given the low 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 tool's purpose: 'Validates an email address using an external email validation API of abstractapi.' It specifies the verb ('validates'), resource ('email address'), and method ('external email validation API'), distinguishing it from sibling tools like 'check_email_reputation' which likely focuses on reputation rather than validation, and 'validate_phone' which handles a different resource type.

  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 usage for email validation but does not explicitly state when to use this tool versus alternatives like 'check_email_reputation'. It mentions checking 'validity, deliverability, and other attributes', which suggests use cases, but lacks explicit guidance on when to choose this over siblings or when not to use it (e.g., for simple format checks only).

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

• validate_phoneA
  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 of behavioral disclosure and does so comprehensively. It explains what the tool returns (detailed validation results), includes error handling information (raises section), describes the external API dependency, and provides a complete example of the return format. This goes well beyond basic functional description.

  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 sections (purpose, parameters, returns, example, raises) but is somewhat lengthy. Every section adds value, though some information could be more concise. The front-loaded purpose statement is clear, and the structure helps with comprehension despite the length.

  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 tool with no annotations, no output schema, and 0% schema description coverage, the description provides exceptional completeness. It covers purpose, parameters, return values with detailed structure, examples, error handling, and external dependencies. The return value documentation effectively substitutes for a missing output schema, making this description highly complete.

  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?

  With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains both parameters thoroughly: 'phone' is the number to validate, and 'country' is an optional ISO code that helps with analysis. The description includes examples showing how both parameters work, adding significant value beyond the bare 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 clearly states the tool's purpose: 'Validates a phone number using Abstract API's Phone Validation service.' It specifies the exact action (validate), resource (phone number), and service provider, distinguishing it from sibling email tools. The description goes beyond the tool name by explaining it checks validity and returns detailed information.

  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 context about when to use this tool: for validating phone numbers from over 190 countries. It doesn't explicitly mention when not to use it or compare with alternatives, but the context is sufficiently clear given the tool's specialized function. The examples show usage patterns with and without the optional country parameter.

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