Server Quality Checklist

Tool Scores

• get_applicationsC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description must disclose behavioral traits. It only states that the tool returns certain fields and is not a usability check. It does not mention read-only nature, rate limits, authentication, or any side effects. This is minimal disclosure for a tool with no annotation support.

  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 concise (two sentences), with the first sentence stating the core purpose and the second adding result details and a clarification. No unnecessary words. Could be slightly more structured with bullet points or explicit parameter list, but effective as is.

  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 tool has 6 parameters, no output schema, and no annotations, the description is incomplete. It fails to explain all parameters (especially limit and usage_method), provides no return structure details beyond a few fields, and offers no usage guidance. More information is needed for an agent to use it correctly.

  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?

  With schema description coverage at 0%, the description partially compensates by mentioning search criteria such as crop, pest, pesticide_name, and register_no. However, it does not explain the 'limit' or 'usage_method' parameters. The description adds some meaning beyond the schema but leaves gaps.

  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 that the tool searches application information by crop, pest, pesticide name, registration number, etc. It also specifies that results include usage timing, method, and number of uses, and explicitly says it is not a judgment of usability. This differentiates it from sibling tools like check_registered_use, but could be more explicit about exact distinctions.

  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 mentions that the tool is 'not a judgment of usability', implying that for usability decisions, another tool (e.g., check_registered_use) should be used. However, it does not explicitly state when to use this tool versus alternatives, nor provide any conditional advice or exclusions.

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

• search_pesticidesC
  2.9/5.0
  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 only states that the tool searches and returns registration information. It does not disclose whether it is read-only, any authentication or rate limits, or the behavior when multiple parameters are given. For a search tool, more behavioral details are needed.

  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 only two sentences. The first sentence clearly states the purpose and the searchable fields. The second adds a critical caveat. It is front-loaded with the action and resource. No unnecessary words.

  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 that the tool has 5 optional parameters with no output schema and no annotations, the description is insufficient. It does not explain pagination (limit), how the search works (e.g., fuzzy matching, exact match), or what fields are in the output. The caveat is helpful but does not compensate for the missing behavioral and parameter details.

  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%, and the description only lists the parameter names (農薬名・登録番号・有効成分・農薬の種類) without explaining their format, constraints, or how they combine (e.g., AND or OR). The 'limit' parameter is mentioned in the schema but not in the description. The description adds minimal semantic value beyond the field names.

  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 that the tool searches basic pesticide information by name, registration number, active ingredient, or pesticide type. It explicitly mentions the resource (pesticide basic information). However, it does not explicitly differentiate from siblings like 'get_pesticide_by_register_no' or 'check_registered_use', but the broader search scope is implied.

  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 provides a usage caveat: '結果は登録情報の検索結果であり、使用可否の判定ではない。' This tells the agent that the result is not a judgment of usability. However, it does not provide explicit guidance on when to use this tool vs. alternatives or when not to use it beyond the caveat.

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

• dataset_statusB
  3.4/5.0
  Behavior3/5

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

  No annotations provided, so the description carries the full burden. It indicates a read-only operation returning status info, but lacks details on potential side effects, cost, or what happens if the dataset is not available.

  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?

  A single sentence is concise and front-loaded. However, it could be slightly expanded to clarify the dataset scope without losing conciseness.

  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?

  With no output schema, the description covers return values (count, source, date/time). It lacks specification of which dataset, but in context of sibling tools, it is mostly complete.

  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?

  No parameters exist, so baseline 4 applies. The description adds no parameter information, which is acceptable given zero parameters.

  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 returns dataset import status including count, source, and date/time. However, it does not explicitly specify which dataset, relying on context from sibling tools.

  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?

  No guidance on when to use this tool versus alternatives. While siblings are distinct, there is no explicit when/when-not advice or prerequisites.

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

• check_registered_useA
  3.5/5.0
  Behavior4/5

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

  With no annotations, the description carries full behavioral transparency burden. It discloses that output candidates include 'match_confidence' and 'matched_fields', and that it never returns fields like 'allowed'. This informs the agent about the non-deterministic, confidence-based nature of results. It also warns to check product labels, adding safety context. No contradictions with absent annotations.

  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 (4 sentences) and front-loaded with the primary purpose. Every sentence adds unique value: core function, output fields, what it does not return, and a safety warning. No wasted words.

  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 5 parameters, no output schema, and 0% schema description coverage, the description is incomplete. It fails to explain parameter meanings, expected values, or constraints. It mentions output fields but not the full return structure. A more complete description would clarify parameter usage and return format.

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

  Parameters1/5

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

  Schema has 0% description coverage for 5 parameters. The description provides no explanation of any parameter (crop, pest, limit, usage_method, pesticide_name_or_register_no). The agent must infer semantics solely from parameter names, which is insufficient. The description does not compensate for the lack of schema descriptions.

  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 'candidates of registration rows' matching conditions, using a specific verb ('返す' - returns) and resource ('登録行の候補'). It explicitly distinguishes from sibling tools by clarifying it is not a usability judgment, and the sibling tools (e.g., get_applications, search_pesticides) have 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?

  The description implies usage context by stating it is not a usability judgment, suggesting the tool is for exploratory candidate retrieval rather than definitive answers. However, it does not explicitly state when to use this tool vs. siblings, nor provide when-not-to-use or alternatives. The warning to verify before actual use is a post-hoc guideline.

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

• get_pesticide_by_register_noA
  4.7/5.0
  Behavior4/5

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

  With no annotations provided, the description discloses that the output is a summary (total count and example crops/pests), not full application details. This adds behavioral context beyond the schema. It could mention whether the operation is read-only, but the context strongly implies it is read-only, and no side effects are mentioned.

  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 with three focused sentences. The first states the core purpose, the second explains input format, and the third clarifies output scope and directs to the sibling tool. Every sentence adds value 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?

  For a tool with one required parameter and no output schema, the description covers all essential aspects: purpose, input format, output scope, and relationship to sibling tools. It is complete and sufficient for an agent to select and invoke the tool correctly.

  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 provides the parameter 'register_no' with only type 'string'. The description compensates by explaining that it accepts both formats like '12345' and '0012345', adding semantic meaning beyond the schema's minimal definition. Since schema coverage is 0%, this is valuable.

  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 that it retrieves basic pesticide information and a summary of application data based on a registration number. It differentiates itself from sibling tools like 'search_pesticides' (which searches by other criteria) and 'get_applications' (which provides detailed application records).

  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 advises that for detailed application lines, users should use 'get_applications' with the registration number. It also specifies the accepted input formats for the registration number, providing clear when-to-use and when-not-to-use guidance.

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