Server Quality Checklist

Tool Scores

• starting_wordsB
  3.1/5.0
  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 adds relevant context by explaining that the guesses are 'rich in common, distinct letters,' giving insight into the output's nature. However, it does not disclose the return format (e.g., a list of strings), language constraints, or how max_results affects the behavior. It is partially transparent but lacks completeness.

  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 a single, front-loaded sentence that efficiently conveys the tool's purpose. It contains no filler or redundant information, making it appropriately concise and well-structured.

  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 simplicity (one parameter, no output schema, no annotations), the description provides a basic understanding of its purpose and the nature of its suggestions. However, it does not specify the return format, the word source, or how it relates to sibling tools, leaving some gaps in context for an agent to invoke it correctly.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema provides only the parameter name 'max_results' with a default of 15 and no description. The tool description does not mention this parameter at all, so it adds no explicit meaning beyond the schema. There is an implied connection between 'strong opening guesses' and limiting results, but the description fails to clarify the exact role of max_results.

  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 function: suggesting strong opening guesses. It uses the specific verb 'suggest' and identifies the resource as guesses for starting words. It distinguishes itself from siblings by focusing on 'opening guesses' rather than general word suggestions or validation, though it could be more explicit about the word game context.

  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?

  There is no explicit guidance on when to use this tool versus the sibling tools suggest_words or validate_word. The description implies it is for opening guesses, but it does not clarify when to choose this over alternatives, nor does it provide any exclusions or prerequisites. The agent is left to infer usage context.

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

• validate_wordA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description must disclose behavior. It indicates a read-only validation operation via 'Check', but doesn't detail edge cases, case sensitivity, or the exact return type. This is minimal but not misleading.

  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 a single, front-loaded sentence that gets straight to the point. No unnecessary information.

  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 simple tool (one parameter, no output schema), the description is sufficient for understanding the tool's purpose and parameter constraint. It could mention return format, but the verb 'Check' implies a boolean answer.

  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 schema only defines 'word' as a string with no description. The tool description adds key constraints: the word must be 5 letters and is a Wordle guess. This compensates for the 0% 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 validates a word against Wordle's accepted guesses. It uses the specific verb 'Check' and names the resource, distinguishing it from sibling tools that suggest or start words.

  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 the tool is for verifying whether a specific word is a valid Wordle guess. It doesn't explicitly mention when not to use it or alternatives, but the context of validation versus suggestion is clear.

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

• suggest_wordsA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries the burden and does a good job: it discloses the ranking heuristic, the opening-word fallback, and the subtle duplicate-letter handling. This is substantive behavioral context that goes beyond the name and 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 concise and front-loaded with the core purpose. Each sentence adds meaningful detail—usage, ranking, and an important edge case—without waste or redundancy.

  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 there is no output schema and no annotations, the description covers the essential context: what the tool does, how to pass inputs, ranking behavior, no-guess behavior, and duplicate handling. It doesn't mention what happens if no words match or the exact return format, but these are minor gaps for a suggestion tool.

  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?

  Schema description coverage is 0% for top-level parameters, so the description compensates by explaining what 'guesses' should contain (all guesses plus tile colors) and how feedback is interpreted. It does not address 'max_results', but that parameter is relatively self-explanatory.

  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 primary function: returning five-letter words consistent with prior guesses. It distinguishes itself from siblings by focusing on guess-consistency, while the no-guess fallback to opening words is clearly described.

  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 explicit instructions on when to use the tool: pass every guess with the tile colors Wordle showed. It also clarifies behavior when no guesses exist, but it doesn't explicitly mention alternatives like 'starting_words' or 'validate_word' for comparison.

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