Server Quality Checklist

Tool Scores

• discover_filmsA
  3.8/5.0
  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 adds useful context such as defaults (TMDB service, pagination defaults), constraints (max_results: 100), and error conditions (raises ValueError, RuntimeError, ConnectionError). However, it doesn't cover aspects like rate limits, authentication needs, or data freshness, leaving some gaps for a mutation-free 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 well-structured with sections for Args, Returns, and Raises, making it easy to parse. It's appropriately sized for the tool's complexity, though the initial sentence could be more front-loaded with key information, and some details in the Returns section might be redundant if an output schema exists.

  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 the tool's moderate complexity (6 optional parameters, no annotations, but has output schema), the description is largely complete. It covers parameters thoroughly, includes return format details (though output schema may handle this), and mentions error conditions. Minor gaps include lack of sibling tool differentiation and some behavioral aspects like rate limits.

  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 schema description coverage is 0%, so the description must fully compensate. It comprehensively documents all 6 parameters with clear explanations, examples (e.g., '2024', 'en'), enumerated options for sort_by, defaults, and constraints (max: 100). This adds significant meaning beyond the bare input schema.

  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 tool 'discovers films' with optional filters like genre, release year, language, and sorting preferences, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'discover_television' or 'list_genres', which would be needed for a score of 5.

  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 through the mention of 'optional filters' and references 'list_genres' to find genre IDs, providing some context. However, it lacks explicit guidance on when to use this tool versus alternatives like 'discover_television' or 'categorize_genres', and doesn't specify prerequisites or exclusions.

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

• list_genres_simplifiedA
  3.8/5.0
  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 key traits: it uses 'LLM sampling' with a fallback to 'direct extraction,' returns a 'formatted string,' sorts the list, and handles errors by logging and falling back. This covers method, output format, and error handling, though it could mention performance or rate limits for a higher score.

  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 and front-loaded with the core purpose. Each sentence adds value: method details, output format, and error handling. It's appropriately sized for the tool's complexity, but minor verbosity in explaining fallback and errors slightly reduces conciseness, preventing a perfect score.

  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 the tool's moderate complexity (0 params, no annotations, but with output schema), the description is largely complete. It explains the method, output, and error handling. Since an output schema exists, it doesn't need to detail return values further. However, it could briefly mention the sibling tool 'list_genres' for better context, leaving a small gap.

  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 has 0 parameters with 100% coverage, so no parameter information is needed. The description appropriately focuses on behavior and output without redundant param details. It earns a baseline 4 for compensating with clear operational context, though it doesn't add param-specific semantics since none exist.

  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 tool's purpose: 'Get a simplified list of available genre names.' It specifies the verb ('Get') and resource ('genre names'), and distinguishes it from the sibling 'list_genres' by emphasizing 'simplified' and 'without IDs or media type flags.' However, it doesn't explicitly contrast with other siblings like 'categorize_genres' or 'discover_films,' 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 Guidelines3/5

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

  The description implies usage by mentioning 'simplified list' and fallback behavior, suggesting it's for when a clean, formatted output is needed. However, it lacks explicit guidance on when to use this tool versus alternatives like 'list_genres' or other siblings, and no exclusions or prerequisites are stated, making the guidance incomplete.

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

• categorize_genresA
  4.1/5.0
  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 discloses key behavioral traits: it uses a 'hybrid approach' combining hardcoded mappings and LLM-based categorization, and specifies the return format. However, it lacks details on performance, rate limits, or error handling.

  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 front-loaded with the core purpose, followed by implementation details and return format. It is appropriately sized with no wasted sentences, though the return example is detailed but necessary for clarity.

  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 the tool's complexity (hybrid approach), no annotations, and an output schema (implied by the detailed return example), the description is largely complete. It explains the categorization method and output, though could benefit from more context on limitations or edge cases.

  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 has 0 parameters with 100% coverage, so no parameter details are needed. The description appropriately focuses on the tool's function and output, adding value beyond the empty schema without redundancy.

  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 with specific verbs ('Categorize', 'Groups') and resources ('all available genres', 'entertainment genres'), and distinguishes it from siblings like 'list_genres' and 'list_genres_simplified' by focusing on mood-based categorization rather than listing.

  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 by mentioning 'edge cases and unknown genres', but does not explicitly state when to use this tool versus alternatives like 'list_genres' or 'discover_films'. No exclusions or prerequisites are provided.

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

• discover_televisionA
  4.5/5.0
  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: defaults to TMDB service, pagination with page numbers, result limits (max 100), error conditions (ValueError, RuntimeError, ConnectionError), and the complete return structure. The only minor gap is not explicitly stating this is a read-only operation, though 'discovers' implies it.

  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 (Args, Returns, Raises) and front-loads the core purpose. While comprehensive, it could be slightly more concise by integrating some parameter details into the opening sentence rather than separate sections, but every sentence adds necessary value.

  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 (6 parameters, no annotations, but has output schema), the description is complete. It covers purpose, all parameters with semantics, return values (though output schema exists, the description provides helpful context), error conditions, and service defaults. No significant gaps remain 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, the description fully compensates by providing comprehensive parameter documentation. Each of the 6 parameters is clearly explained with examples, default values, constraints (e.g., 'max: 100'), and usage guidance (e.g., 'use list_genres to find IDs'). The description adds 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: 'Discovers television shows based on optional filters' with specific resources (television shows) and actions (discover with filtering). It distinguishes from sibling tools like 'discover_films' by specifying television shows and from 'list_genres' by focusing on discovery rather than listing.

  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 for when to use this tool (discovering TV shows with filtering) and mentions using 'list_genres' to find genre IDs, which implies an alternative tool for that purpose. However, it doesn't explicitly state when NOT to use this tool or compare it directly with 'discover_films' for media type selection.

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

• list_genresA
  4.5/5.0
  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 specifying the return format as a dictionary mapping genre names to properties. It discloses behavioral traits like the structure of returned data (genre names with id, has_films, has_tv_shows), which is valuable beyond basic listing. It doesn't mention potential limitations like rate limits or auth needs, but for a read-only list tool, this is acceptable.

  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 sized and front-loaded: the first sentence states the purpose clearly, followed by a concise specification of the return format. Every sentence earns its place by providing essential information without redundancy.

  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 low complexity (0 parameters, read-only operation), the description is complete. It explains what the tool does and the return format in detail. Since an output schema exists, the description doesn't need to explain return values further, and it adequately covers the tool's purpose and behavior.

  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 has 0 parameters with 100% coverage, so the baseline is 4. The description adds no parameter information, which is fine since there are no parameters to document. It doesn't detract from the schema, maintaining the baseline score.

  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 verb 'List' and resource 'all available entertainment genres across media types and providers.' It distinguishes from siblings like 'list_genres_simplified' by specifying comprehensive coverage across media types and providers, and from 'categorize_genres' by focusing on listing rather than categorization.

  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 implies usage context by mentioning 'across media types and providers,' suggesting this tool is for broad genre discovery. However, it lacks explicit guidance on when to use this vs. 'list_genres_simplified' or alternatives like 'discover_films' for specific media types, which would be needed for a perfect score.

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

• compare_llm_responsesA
  4.6/5.0
  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 and does well by disclosing key behaviors: parallel execution ('sends...in parallel'), default values (Ollama as second agent, specific defaults), error handling (error fields in response), and exception conditions (raises ValueError). It doesn't mention rate limits or authentication needs.

  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 sized and well-structured: purpose statement first, then execution details, followed by parameter explanations, return format, and error conditions. Every sentence adds value with zero wasted text.

  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 (parallel LLM calls with comparison), no annotations, and 0% schema coverage, the description provides complete context. It explains purpose, behavior, all parameters, return format (detailed dictionary structure), and error conditions. The output schema exists but the description still adds useful semantic context.

  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 explaining all 4 parameters in detail: 'prompt' (what to send), 'llm_model' (which second model with default), 'temperature' (for both LLMs with default), and 'max_tokens' (maximum tokens with default). It adds meaning 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 with specific verbs ('compare', 'sends') and resources ('Claude and a second agent', 'structured comparison of their responses'). It distinguishes itself from sibling tools by focusing on LLM response comparison rather than media categorization or discovery.

  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 for when to use this tool ('compare how Claude and a second agent respond to the same prompt'), but doesn't explicitly state when not to use it or mention alternatives. The sibling tools are unrelated (media categorization/discovery), so no direct alternatives are needed.

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