Server Quality Checklist

Tool Scores

• read_articlesA
  4.2/5.0
  Behavior4/5

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

  Annotations already mark this as read-only and idempotent, so the safety profile is known. The description adds behavioral nuance by labeling 'sections' as 'lightweight' and explaining that 'get' retrieves 'full content,' which helps the agent understand performance and output characteristics without contradicting the 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?

  A single, information-dense sentence that front-loads the core function and uses a colon to enumerate the four modes. Each mode has a brief but meaningful explanation, and there is no redundant filler.

  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?

  For a read tool with strong annotations and a fully-described schema, the description covers the main usage scenarios and explicitly links to the article-creation workflow. It lacks details about return values, but the absence of an output schema and the read-only nature make this acceptable.

  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 100% with detailed parameter descriptions. The tool description adds a mode-oriented overview (e.g., 'search' for full-text, 'list' to browse by section or labels) that helps map parameters to modes, but it doesn't provide additional syntax beyond what the schema already documents.

  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 opens with a specific verb and resource: 'Read articles from the Zendesk Help Center (Guide).' It then details four distinct modes, making it clear this is a read tool for articles, distinguishing it from sibling write tools like create_article and update_article.

  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 explicit use cases for each mode, notably the sections mode: 'use this to discover section IDs before creating articles,' which directly ties to the sibling create_article tool. While it doesn't explicitly contrast with fetch_tickets, the article focus and write-tool siblings make the context clear.

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

• fetch_ticketsA
  4.3/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint and idempotentHint, and the description adds meaningful behavioral detail by explaining that the tool returns ticket metadata plus complete conversation transcripts, and supports date/status filters. There is no contradiction with the 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 three sentences, front-loaded with the main purpose, then output specifics, and a stated use case. Every sentence earns its place and there is no 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 read-only fetch tool with no output schema, the description explains the return content (metadata and transcripts), filters, and usage context. Combined with rich schema descriptions and annotations, there are no major contextual gaps.

  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 100% with each parameter fully described, including defaults and accepted formats. The description only restates the filtering capability without adding additional parameter-level detail, so the schema carries the semantic burden.

  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 opens with a specific verb and resource ('Fetch Zendesk support tickets') and immediately distinguishes the tool by its key feature: full conversation transcripts. It clearly differentiates itself from the article-focused sibling tools by domain and purpose.

  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 explicitly states the intended use case: 'Use for analyzing support trends, common issues, and customer sentiment.' It doesn't mention when not to use it or name alternatives, but the context is clear and sufficient for an agent.

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

• create_articleA
  4.4/5.0
  Behavior4/5

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

  Beyond the annotations (readOnly=false, idempotent=false), the description discloses a key behavioral trait: 'The article is created as a draft by default to allow review before publishing.' This adds context not captured in the annotation hints, though it doesn't detail error cases or response structure.

  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, front-loaded with the core purpose, and contains no redundant words. Every sentence earns its place, with the second sentence providing crucial usage guidance.

  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?

  For an 8-parameter tool with no output schema, the description covers the essential context: the action, the draft default, and the prerequisite section_id discovery. However, it does not mention what the response contains (e.g., the created article with ID), which would improve completeness. Still, the schema is rich and the guidance is solid.

  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?

  All 8 parameters are documented in the schema (100% coverage), so the baseline is 3. The description only restates the draft default and section_id requirement, which are already present in the schema descriptions. It adds no new parameter-level semantics.

  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 'Create a new article in the Zendesk Help Center', identifying the specific action (create) and resource (article). It differentiates from siblings like update_article (modifies existing) and read_articles (reads), making the purpose unambiguous.

  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 says 'Requires a section_id — use read_articles with include_sections=true to discover available sections.' This tells the agent exactly when to use this tool and how to obtain a prerequisite using a sibling, which is strong guidance.

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

• update_articleA
  4.4/5.0
  Behavior4/5

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

  Annotations already indicate it is not read-only and is idempotent, but the description adds useful behavioral context: it uses the Zendesk Translations API and operates on a specific locale. It also discloses a usage requirement (at least one field) that is not present in the annotations. It does not mention permissions or error handling, but the bar is lower due to existing 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 only two sentences long and front-loads the core purpose in the first sentence. The second sentence provides an essential usage constraint without any unnecessary words. Every phrase adds value, making it both concise and well-structured.

  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 moderate complexity (5 parameters, no output schema), the description covers purpose, the API used, and a key operational constraint. It does not specify the return value or potential errors, but the combination of annotations and schema covers most of the necessary context, making it suitably complete for an agent to select and invoke 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 describes all parameters, but the description adds a crucial collective requirement ('At least one of title, body, or draft must be provided') that is not obvious from the schema alone. It also clarifies the role of the locale parameter by mentioning the Translations API, enhancing the value beyond the individual parameter 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 verb ('Update') and the specific resource ('existing Zendesk Help Center article') along with the content fields affected (title, body, draft status). This distinguishes it from siblings like 'create_article' (create) and 'read_articles' (read), leaving no ambiguity about what the tool does.

  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 that this tool is for modifying existing articles and includes a specific constraint ('At least one of title, body, or draft must be provided'). It does not explicitly name sibling alternatives, but the 'existing' wording and the tool's purpose imply when it should be used versus create/read tools, so it nearly meets the 5 threshold.

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