Server Quality Checklist

Tool Scores

• get_quality_standardsC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what the tool returns but doesn't describe important behavioral aspects like whether it's read-only, if it requires authentication, rate limits, error conditions, or pagination behavior. The description is minimal and lacks operational context needed for safe invocation.

  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 well-structured with clear sections (purpose, Args, Returns) and uses minimal sentences. Each section earns its place by providing essential information without redundancy. However, the Args section could be more integrated with the main description rather than appearing as a separate block.

  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 has an output schema (which handles return values) and moderate complexity with 3 parameters, the description is minimally adequate. It covers basic purpose and parameters but lacks important context about behavioral traits, usage guidelines, and detailed parameter semantics that would make it complete for safe and effective use.

  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 description lists parameters in the Args section with brief explanations, but with 0% schema description coverage, it doesn't fully compensate. It provides basic meaning for 'language', 'framework', and 'category' but lacks details on format constraints, valid values beyond examples, or how parameters interact. The schema shows defaults and requirements, but the description adds only marginal semantic value.

  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 purpose with a specific verb ('Retrieve') and resource ('quality standards for a language/framework combination'). It distinguishes from siblings like 'validate_code_quality' by focusing on retrieval rather than validation. However, it doesn't explicitly contrast with 'get_verification_checklist' which might have overlapping retrieval functionality.

  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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_verification_checklist' or 'validate_code_quality' that might serve similar purposes, nor does it specify prerequisites, constraints, or appropriate contexts for usage beyond the basic parameter requirements.

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

• validate_code_qualityB
  3/5.0
  Behavior2/5

  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 mentions validation against standards but doesn't describe what happens during validation (e.g., external calls, processing time), error handling, or output format details beyond the basic return statement. This leaves significant gaps for a tool with 6 parameters.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness3/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is appropriately sized but not optimally structured. The initial sentence is clear, but the parameter and return sections are listed without integration into a cohesive narrative. While efficient, it could be more front-loaded with key usage context.

  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 complexity (6 parameters, no annotations) and the presence of an output schema, the description is moderately complete. It covers parameter semantics well but lacks behavioral context and usage guidelines. The output schema likely details return values, reducing the need for that in the description, but overall gaps remain.

  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 description adds substantial meaning beyond the input schema, which has 0% description coverage. It explains each parameter's purpose (e.g., 'code: The code to validate,' 'check_security: Validate against security standards'), including enum values for 'language' and 'severity_threshold.' This compensates well for the schema's lack of documentation.

  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 purpose as 'Validate generated code against quality standards,' which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'validate_plan_quality' or 'get_quality_standards,' leaving some ambiguity about when to choose this tool over alternatives.

  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 provides no guidance on when to use this tool versus its siblings. It doesn't mention alternatives like 'validate_plan_quality' for non-code validation or 'get_quality_standards' for retrieving standards, nor does it specify prerequisites or contextual constraints for usage.

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

• analyze_intentB
  3.2/5.0
  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 mentions the tool analyzes a prompt and returns structured intent analysis, but doesn't disclose behavioral traits such as whether it's read-only, requires authentication, has rate limits, or what happens with invalid inputs. This is a significant gap for a tool with no annotation coverage.

  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 appropriately sized and front-loaded, with the core purpose stated first. The additional details about args and returns are useful but could be more integrated. There's no wasted text, though it could be slightly more polished for optimal clarity.

  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 moderate complexity (1 parameter, no annotations, but has an output schema), the description is minimally complete. It covers the purpose and basic I/O, but lacks details on behavior, error handling, or how it differs from siblings. The presence of an output schema reduces the need to explain return values, but more context is needed for full understanding.

  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 description adds minimal semantics beyond the input schema: it defines 'prompt' as 'The developer prompt to analyze'. With 0% schema description coverage and only 1 parameter, this provides some value, but it's basic and doesn't elaborate on format, constraints, or examples. The baseline is 3 since the schema covers the parameter structure adequately.

  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 purpose: 'Analyze a prompt without enhancement, returning the detected intent, entities, and recommended approach.' This specifies the verb (analyze), resource (prompt), and output (intent, entities, approach). However, it doesn't explicitly differentiate from sibling tools like 'enhance_prompt' or 'suggest_tools', which prevents a perfect score.

  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 by mentioning 'without enhancement' and listing output components, suggesting it's for raw analysis rather than enhancement. However, it lacks explicit guidance on when to use this tool versus alternatives like 'enhance_prompt' or 'suggest_tools', and doesn't specify prerequisites or exclusions, leaving some ambiguity.

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

• get_verification_checklistB
  3.2/5.0
  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 of behavioral disclosure. It states the tool 'Returns: Verification checklist appropriate for the task,' which implies a read-only operation, but doesn't disclose critical traits like whether it's idempotent, has rate limits, requires authentication, or what format the checklist returns. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

  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 appropriately sized and front-loaded, starting with the core purpose. The 'Args' and 'Returns' sections add necessary details without redundancy. However, the structure could be slightly improved by integrating the parameter explanations more seamlessly, and the second sentence ('Args:...') feels slightly detached, but overall it's efficient with minimal waste.

  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 tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is reasonably complete. It explains the purpose, parameters, and return value, and the presence of an output schema means it doesn't need to detail return values further. However, it lacks behavioral context (e.g., error handling or usage scenarios), which slightly reduces completeness 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.

  Parameters4/5

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

  Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics beyond the schema by explaining that 'task_type' includes specific values (generation|refactor|debug|review|test) and 'touches_security' indicates 'Whether the task involves security-sensitive code.' This clarifies parameter purposes and constraints, though it doesn't cover all possible nuances like default behavior for 'touches_security' (which the schema sets to false).

  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 purpose: 'Get a verification checklist for a specific task type.' It specifies the verb ('Get') and resource ('verification checklist'), and distinguishes it from sibling tools like 'get_quality_standards' or 'validate_code_quality' by focusing on task-specific checklists rather than general standards or validation. However, it doesn't explicitly differentiate from all siblings, such as 'suggest_tools', which might also relate to task guidance.

  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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for selection, or exclusions, and fails to reference sibling tools like 'get_quality_standards' or 'validate_plan_quality' that might overlap in purpose. Usage is implied only by the tool's name and description, with no explicit when/when-not instructions.

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

• validate_plan_qualityA
  3.6/5.0
  Behavior3/5

  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 discloses key behavioral traits: it returns a quality score, issues list, and a 'ready_for_cheap_model' flag, which adds context beyond basic validation. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a tool with no annotation coverage.

  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 appropriately sized and front-loaded, starting with the core purpose. The 'Args' and 'Returns' sections are structured clearly, but the note about 'cheaper models require stricter plan quality' could be integrated more smoothly. Overall, it's efficient with minimal waste, though minor improvements in flow could elevate it to a 5.

  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 complexity (2 parameters, no annotations, but with an output schema), the description is fairly complete. It explains the purpose, parameters, and return values, and the output schema likely covers return details, so the description doesn't need to elaborate further. However, it could benefit from more context on how validation works or links to sibling tools, keeping it from a perfect score.

  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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'plan' is 'The plan text to validate' and 'target_model' specifies the model that will implement, with options like 'haiku|flash|cheap|capable' and the note that 'cheaper models require stricter plan quality.' This compensates well for the schema's lack of descriptions, though it doesn't detail all possible enum values or constraints.

  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 purpose: 'Validate a plan for implementation by a less capable model.' It specifies the verb ('validate') and resource ('plan'), and distinguishes it from siblings like 'validate_code_quality' by focusing on plan validation rather than code. However, it doesn't explicitly differentiate from all siblings (e.g., 'get_verification_checklist'), so it falls short of a perfect score.

  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 mentioning 'less capable model' and that 'cheaper models require stricter plan quality,' which suggests when to use it based on target model constraints. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_quality_standards' or 'suggest_tools,' nor does it provide exclusions or prerequisites, leaving some ambiguity.

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

• suggest_toolsA
  3.9/5.0
  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 mentions that the tool returns 'tool recommendations with priorities and reasons,' which adds some context about output behavior. However, it lacks details on permissions, rate limits, or error handling, leaving gaps for a tool with no annotation coverage.

  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 well-structured with a purpose statement, arg explanations, and return details, all in a compact format. Every sentence adds value, though the arg descriptions could be slightly more integrated into the flow rather than listed separately.

  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 tool's moderate complexity, no annotations, and the presence of an output schema (which handles return values), the description is fairly complete. It covers purpose, parameters, and output behavior adequately, though it could benefit from more behavioral context like error cases or usage examples.

  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%, so the description must compensate. It provides clear semantics for all three parameters: intent_description explains its purpose, available_mcps notes it's optional and comma-separated, and discover_capabilities describes its boolean nature and effect. This adds significant value beyond the bare 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 with a specific verb ('suggest') and resource ('MCP tools'), and it distinguishes itself from siblings by focusing on tool recommendation rather than analysis, enhancement, or validation. The phrase 'for a given intent' establishes its unique role in the toolset.

  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 when determining which tools to use for an intent, but it doesn't explicitly state when to use this tool versus alternatives like analyze_intent or get_quality_standards. No exclusions or clear alternatives are provided, leaving usage context somewhat vague.

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

• enhance_promptA
  4/5.0
  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. It discloses the tool's behavior by describing what it adds ('quality requirements, codebase context, and tool recommendations') and mentions auto-detection and context levels. However, it lacks details on permissions, rate limits, or error handling, leaving gaps for a tool with no annotation coverage.

  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 appropriately sized and front-loaded, starting with a clear purpose statement. The Args and Returns sections are structured efficiently, with each sentence adding value without redundancy. It avoids unnecessary elaboration.

  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 tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is fairly complete. It covers the tool's purpose, parameters, and return value. The output schema reduces the need to explain returns in detail, but more behavioral context could be added 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.

  Parameters4/5

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

  The schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter's purpose: 'prompt' as the original input, 'task_type' with enum values and override function, and 'context_level' with options. This goes beyond the bare schema, though it could provide more detail on enum 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 the tool's purpose with specific verbs ('enhance a coding prompt') and resources ('quality requirements, codebase context, and tool recommendations'). It distinguishes from siblings like 'analyze_intent' or 'get_quality_standards' by focusing on prompt enhancement rather than analysis or standards retrieval.

  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 through the mention of 'auto-detection' and parameter defaults, but does not explicitly state when to use this tool versus alternatives like 'suggest_tools' or 'validate_code_quality'. It provides some context but lacks clear exclusions or named alternatives.

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