Server Quality Checklist

Tool Scores

• compare_sakeA
  3.5/5.0
  Behavior3/5

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

  No annotations provided; description explains output metric (spread per axis) but does not disclose whether operation is read-only, authentication needs, or rate limits. It partially compensates for missing annotations.

  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?

  Single sentence, front-loaded with 'Compare 2–5 sake', and uses line break effectively. Missing some detail but remains efficient.

  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?

  Output schema exists, so description need not explain return values, but it does mention spread per axis. However, it omits definition of the six flavor axes and parameter usage details, making it insufficient for complete agent guidance.

  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?

  Schema coverage is 0%; description does not explain the single parameter 'brand_ids' beyond implying it identifies sake. Fails to clarify it must contain 2-5 integers or how to obtain them.

  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?

  Description clearly states 'Compare 2–5 sake side by side across all six flavor axes' with specific verb and resource. It distinguishes from sibling tools like 'find_similar_sake' and 'get_sake_profile' which serve different purposes.

  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?

  Description implies usage for comparing 2-5 sake but does not explicitly state when not to use or provide alternatives. No mention of constraints like maximum count or what to do for more than 5.

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

• find_similar_sakeA
  4/5.0
  Behavior3/5

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

  No annotations provided, so description carries full burden. It explains the mode behavior and prerequisite, but lacks details on failure cases, auth requirements, or response structure. The output schema exists but is not referenced.

  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 well-structured with a star emphasis, clear summary, and a bullet list for mode options. Every sentence serves a purpose without fluff.

  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 output schema existence and 3 simple parameters, the description covers core behavior and mode variations well. It could improve by mentioning error conditions or how the limit parameter affects results, but overall it is adequate.

  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 coverage is 0%, but the description adds value for the 'mode' parameter with detailed options. The 'brand_id' and 'limit' parameters receive no added explanation beyond schema titles, so coverage is incomplete.

  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 finds similar sakes by flavor-vector proximity, distinguishing it from siblings like compare_sake and search_sake.

  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 explains when to use (given a sake, find similar), details the mode parameter for direction, and notes a prerequisite (only sakes with a flavor chart). However, it does not explicitly list when not to use or alternatives.

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

• search_sakeA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries full burden. It reveals matching behavior (kanji direct, Chinese kanji sharing glyphs) but does not disclose read-only nature, rate limits, or authentication requirements. Adequate but not comprehensive.

  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?

  Very concise: three sentences with no redundancy. Front-loaded with the core action, then adds search detail and usage guidance efficiently.

  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 output schema exists, description need not detail return values. It covers search logic, filtering, and integration with siblings. Could mention limit's purpose or case sensitivity, but still reasonably complete.

  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 has no descriptions (0% coverage). Description explains 'query' (brand/brewery name) and 'area' (prefecture), but does not explain 'limit'. Adds meaning for two of three parameters, partially compensating for schema gap.

  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 finds sake by brand or brewery name and returns IDs, with specific detail about kanji matching. It distinguishes from sibling tools by mentioning using IDs with get_sake_profile, find_similar_sake, and compare_sake.

  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 explains when to use area filtering and directs the user to other tools for further actions. It does not explicitly state when not to use this tool, but the context implies its role as a search step.

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

• get_sake_profileA
  4.1/5.0
  Behavior4/5

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

  The description discloses that the class is estimated (heuristic) and lists fields the dataset does not provide, preventing invention. Without annotations, it carries the full burden and provides useful transparency, though it could mention permissions or side effects.

  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 two sentences long, front-loaded with the primary outputs, and includes a helpful clarification about missing dataset fields. Every sentence adds value without 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 the output schema exists, the description need not detail return values. It lists key output components and addresses dataset limitations. It lacks error handling or prerequisites but is adequate for a single-parameter tool.

  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 sole parameter brand_id is not explained in the description beyond implying it identifies a sake. With 0% schema description coverage, the description fails to compensate by clarifying what brand_id is or how to obtain it.

  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 returns the six-axis flavor profile of a single sake, including an ASCII radar, flavor tags, an estimated class, and popularity rank. It distinguishes from sibling tools like compare_sake and search_sake by focusing on a single sake's profile.

  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 use when needing a single sake's flavor profile, contrasting with sibling tools for comparison or search. However, it does not explicitly state when not to use it or mention alternatives.

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

• sync_sakenowa_dataA
  5/5.0
  Behavior5/5

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

  Discloses weekly auto-refresh behavior and the effect of force parameter, plus return values (dataset counts, year-month, attribution). No annotations provided, but description fully covers behavioral expectations.

  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?

  Three sentences: purpose, usage guideline, return summary. No redundant text; every sentence adds value. Front-loaded with the primary action.

  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 simplicity, the description covers enough for an agent to know when to invoke it, what it does, how parameters affect behavior, and what to expect in return. Output schema existence further reduces need for return detail.

  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?

  Although schema coverage is 0%, the description explains the only parameter (force=True) and its specific purpose. Single boolean parameter is well-documented with 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 uses a specific verb ('Fetch/refresh') and resource ('Sakenowa open dataset'), clearly distinguishing this data sync tool from sibling tools like compare_sake or search_sake which operate on individual sake items.

  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 states when to use ('rarely needed') and when to use force=True ('to pull the latest monthly snapshot immediately'). Provides clear context and a specific condition for invocation.

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