Server Quality Checklist

Tool Scores

• analyze_course_feedbackA
  3.6/5.0
  Behavior2/5

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

  No annotations are provided, so the description must fully disclose behavioral traits. It describes the analysis as returning structured outputs but does not mention side effects, read-only nature, error handling, or limitations (e.g., input size limits are only in the schema). The description does not contradict annotations (none exist), but it is insufficient for an agent to safely invoke the tool without additional assumptions.

  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 primary purpose and outputs. No extraneous information, perfectly scoped for quick comprehension.

  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 that an output schema exists to document return values and the tool is a non-destructive analysis, the description covers the main purpose, input type, and high-level output categories. It lacks details on error behavior or scalability, but for this type of tool, it is largely 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?

  Input schema coverage is 100%, so baseline is 3. The description adds minimal meaning beyond the schema—it only restates that input is a JSON array of feedback strings. The optional parameters 'course_name' and 'analysis_focus' are not mentioned in the description, missing an opportunity to clarify their purpose.

  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 analyzes a batch of student course feedback and lists specific outputs: sentiment scores, top themes, complaints, praises, and improvement suggestions. It uses a specific verb 'analyze' and resource 'course feedback', and the sibling tools handle unrelated tasks (report generation, outline creation, etc.), so there is no ambiguity.

  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 when to use the tool (when you have a batch of student feedback to analyze) but does not explicitly state when not to use it or provide alternatives. Given the siblings are clearly different, the usage is reasonably implied, but no explicit guidance is given.

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

• triage_support_queryA
  3.6/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden. It does not disclose how classification is performed (e.g., rules or AI), potential latency, or error scenarios. The outputs are listed but not explained beyond names.

  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?

  Two sentences, front-loaded with the core purpose and target audience. No extraneous information; every sentence adds value.

  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 and parameters are clear, the description covers return values and primary use. However, behavioral transparency is missing, making it slightly incomplete for a tool with no annotations.

  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 description coverage is 100%, so baseline is 3. The description adds minimal extra meaning (e.g., 'raw student support message' for query) but does not provide deeper context beyond the 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: analyzing a student support query and returning specific outputs (category, urgency, etc.). It distinguishes from sibling tools (e.g., analyze_course_feedback is for course feedback, not support queries).

  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 identifies the use case (automating support ticket triage) but does not explicitly mention when not to use it or suggest alternatives when the query is not about support.

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

• batch_generate_reportsA
  3.8/5.0
  Behavior2/5

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

  No annotations are provided, so the description must carry the full burden for behavioral disclosure. It does not mention whether the tool is read-only, destructive, requires authentication, or has rate limits. It only describes input and output formats, missing a safety statement.

  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 primary action, and every sentence contributes essential information. No redundant or extraneous content.

  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 presence of an output schema, return values need not be explained. The description covers purpose, input modes, and key output components. It does not mention error handling, prerequisites (e.g., file existence), or performance limits, but overall it is adequate for a batch generation tool.

  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 description coverage is 100%, so baseline is 3. The description adds value by grouping parameters into inline vs CSV modes, but the schema already describes each parameter's role. No additional semantics beyond what the schema provides.

  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 'generate', the resource 'structured weekly student progress report', and the context 'from raw student data'. It also specifies input modes and output components, making it distinct from siblings like 'analyze_course_feedback' or 'create_course_outline'.

  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 for batch report generation by describing input options and output, but it does not explicitly state when to use this tool versus alternatives or provide exclusions. The siblings are distinct enough that confusion is minimal.

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

• create_course_outlineA
  3.9/5.0
  Behavior3/5

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

  No annotations are provided, so the description must cover behavioral traits. It lists what the tool returns but does not disclose how it generates the outline (e.g., AI model used, limitations, or any side effects). The output description is useful but not comprehensive for behavioral transparency.

  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 with no unnecessary words. It front-loads the core purpose and lists output components efficiently. Every sentence earns its place.

  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 has an output schema, the description need not elaborate on return values. It already covers the key output items. With good schema coverage and no nested objects, the description is complete for this moderately complex tool.

  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?

  The input schema has 100% coverage, so the baseline is 3. The description adds no new meaning beyond restating that the tool works for any topic and audience level, which is already implied by the parameter descriptions. No additional semantics provided.

  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 generates a detailed, structured course outline and lists specific output components (modules, lessons, objectives, durations, assessment ideas). It distinguishes itself from sibling tools like analyze_course_feedback or batch_generate_reports which perform different tasks.

  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 the tool can be used for any topic and audience level, but does not explicitly state when to use it over alternatives or provide exclusion criteria. Sibling tools are sufficiently different, so no guidance is needed for distinguishing, but lack of explicit usage context lowers the score.

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

• generate_reengagement_messageA
  4/5.0
  Behavior3/5

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

  No annotations exist, so the description must carry behavioral weight. It states the output includes three components but does not disclose how the message is generated (e.g., AI model usage) or any constraints like token limits or personalization depth.

  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?

  Two sentences efficiently convey the tool's purpose, outputs, and usage context. Every word adds value, with no 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 existence of a separate output schema, the description adequately notes the return types. It covers the core intent and expected outputs, though it could mention the generation methodology or rate limits for completeness.

  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%, so the input schema already details all parameters. The description adds 'personalized' as context but does not enhance meaning beyond schema descriptions. Baseline 3 applies.

  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 explicitly states the tool generates a personalized re-engagement message for an inactive student, listing specific outputs (subject line, email body, WhatsApp-friendly short message). This clearly differentiates it from siblings like 'analyze_course_feedback' or 'triage_support_query' 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 Guidelines4/5

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

  The description includes 'Use for growth team automation,' indicating appropriate context. However, it does not explicitly state when not to use or mention alternatives among siblings, leaving some ambiguity.

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