qa-mcp-server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool targets a distinct aspect of QA workflow (handoff, checkpoints, test cases, reporting, recommendations, validations). No two tools appear to do the same thing.
Naming Consistency5/5
All tools follow a consistent verb_noun snake_case pattern (e.g., create_test_case, validate_context_health), making it easy to predict functionality.
Tool Count5/5
15 tools is well-scoped for a QA server covering test creation, execution, reporting, defect tracking, and stage management. Each tool serves a clear purpose.
Completeness4/5
Core workflows (create, find, execute, report, validate) are covered. Minor gaps include missing update/delete operations for test cases, but the surface is sufficient for the intended structured process.
Average 2.5/5 across 15 of 15 tools scored. Lowest: 1.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but mentions nothing about behavior (e.g., read-only, destructive, auth needs). The tool might be a read operation, but that is not stated, making it unsafe for the agent to use.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is under-specification rather than conciseness. It fails to convey essential information given the tool has 3 parameters and no other structured documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool lacks output schema, parameter descriptions, and behavioral information. For a search/find operation with multiple filters, the description should provide details on expected returns, but it provides none.
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?
The input schema has 0% parameter description coverage, and the description does not explain what 'ticket_id', 'filter_tags', or 'filter_priority' mean or how they affect results. The agent gets no semantic help beyond the parameter names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Find existing test cases for reuse' provides a general idea but lacks specificity. It doesn't differentiate from siblings like 'save_test_cases' or 'create_test_case' which could also involve existing cases. The purpose is vague, not clearly indicating the scope or distinction.
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 is provided on when to use this tool versus alternatives. The description lacks context about what scenarios this tool is appropriate for, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 does not mention any side effects, authentication needs, or what happens on failure. The description is too minimal to convey any 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.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at one sentence, but it is under-specified. Conciseness is positive only when paired with sufficient information; here it sacrifices completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of many sibling validation tools and the lack of an output schema, this description is completely inadequate. It does not explain return values, behavior, or when to use this tool, making it nearly useless for an AI agent.
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?
The schema has three parameters with no descriptions (0% coverage), and the description adds no meaning to them. It does not explain what stage, output, or require_validation mean, leaving the agent without essential usage details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool validates output before moving to the next stage, giving a clear verb and resource. However, it lacks specificity about what kind of output and what validation entails, and it does not distinguish from similar sibling tools like validate_context_health or validate_execution_readiness.
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 is provided about when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or context for its use among the many sibling validation tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 does not indicate side effects (e.g., that this is a write operation that creates a new record), required permissions, rate limits, or whether the operation is idempotent. The minimal description fails to add cautionary or operational context.
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 one efficient sentence with no wasted words, which aligns with conciseness. However, it sacrifices necessary detail—for a tool with many parameters and siblings, brevity leads to inadequacy. It earns a middle score for being clean but under-informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 7 parameters (6 required), no output schema, no annotations, and several sibling tools. The single-sentence description leaves the agent with insufficient context to use the tool correctly. It does not explain the meaning of each parameter, required input formats, or what happens upon success/failure. This is critically incomplete.
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 description coverage is 0%, meaning none of the 7 parameters (6 required) are explained in the description. The description adds no meaning beyond the raw schema, so the agent must infer the purpose of each field. This is a critical gap given the number and requirements.
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 uses a specific verb-resource pair ('record the result of a test execution') which makes the general purpose clear. However, it lacks additional details that would distinguish it from sibling tools like 'report_defect' or 'validate_qa_output', which might also involve recording results. A more precise scope would elevate it to a 5.
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 is provided. The description does not mention prerequisites, when not to use it, or suggest any sibling tools for different scenarios. This leaves the agent without decision-making support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 disclosing behavioral traits. It only states the purpose ('save test cases') but does not reveal whether the operation is creating, updating, or overwriting, nor does it mention side effects, permissions, or error conditions. This is insufficient for an agent to predict the tool's impact.
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 a single concise sentence with no redundant words, but it sacrifices essential detail for brevity. While front-loaded with the action, it does not earn its place by providing sufficient information, resulting in a minimal but incomplete description.
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 lack of annotations, output schema, and parameter guidance, the description is far from complete. The tool handles structured data (array of test cases with multiple fields) and requires context about validation, return values, and error handling to be used effectively. The current description leaves critical gaps.
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?
With schema description coverage at 0%, the description adds no meaning to the parameters. It does not explain what 'ticket_id' represents or describe the structure of 'test_cases' (e.g., the significance of fields like 'id', 'steps', or 'priority'). An agent must rely solely on the raw schema, which lacks explanatory context.
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 uses the verb 'save' and identifies the resource as 'test cases', clearly indicating the tool's primary action. It also specifies the context 'for future reuse on bug fixes/regressions', giving a sense of purpose. However, it does not differentiate between saving a single test case (as might be done by create_test_case) or a batch, leaving some ambiguity in its scope.
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 explicit guidance on when to use this tool versus alternatives like create_test_case or find_reusable_test_cases. It implies usage for saving test cases for later use but offers no exclusions, prerequisites, or comparisons with sibling tools, leaving an AI agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must bear the full burden of behavioral disclosure. It implies a read-only check but does not explicitly state side effects, prerequisites, or safety profile. The brief description is insufficient for safe agent invocation.
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 extremely concise—a single phrase—but lacks structure or additional context. While brevity is valued, it sacrifices informativeness, making it minimally adequate.
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's simplicity (one required parameter, no output schema), the description fails to clarify return values, interpretation of results, or what constitutes 'clean' vs. 'bloated'. The agent lacks critical context for effective use.
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 description coverage is 0%, and the description adds no information about the 'ticket_id' parameter beyond its type. The agent cannot infer format, purpose, or constraints from the description alone.
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 'Check if context is clean and not bloated' clearly states the tool's action (check) and target (context health). It is specific enough to distinguish from siblings, though 'clean' and 'bloated' lack precise definition.
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 usage guidance is provided. The description does not indicate when to use this tool over sibling validators like 'validate_execution_readiness' or 'validate_qa_output', nor does it mention scenarios where this tool is preferable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only says 'check', which is ambiguous. It does not clarify whether the tool is read-only, requires side effects, or what happens upon invocation. This is insufficient for safe agent use.
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 a single sentence, which is concise, but it is under-specified. It earns its place by stating the core purpose, but additional structured detail is missing. It could be longer to add value while remaining efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and three parameters with zero coverage, the description is severely incomplete. It does not explain return values, validation logic, or how to interpret results, making it inadequate for effective agent use.
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 description coverage is 0%, and the tool description fails to explain any of the three parameters (test_case_count, critical_count, has_test_data). The agent cannot infer their meaning or constraints from the provided text.
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 'Check if test cases are ready for execution' clearly states the verb (check) and resource (test cases readiness), making the purpose understandable. However, it does not differentiate from sibling tools like validate_context_health, which might also assess readiness.
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 is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, conditions, or scenarios where this tool is appropriate, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must disclose behavioral traits. It does not mention idempotency, authentication requirements, side effects, or what happens on duplicate ticket_ids.
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 a single sentence, making it concise, but it lacks structure and depth. It could be expanded with key information without losing brevity.
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's role in QA workflows and the lack of output schema, the description is too minimal. It fails to explain what a 'clean handoff' entails or how the summary is used in the new session.
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 adds no meaning to ticket_id or current_stage. It does not explain how to determine current_stage or format ticket_id, leaving the agent guessing.
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 creates a handoff summary to start a new session, using a specific verb and resource. It distinguishes from siblings like create_session_checkpoint, though it could be more explicit about the content.
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. With 14 sibling tools, the description should indicate when a handoff summary is appropriate instead of a checkpoint or test case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behaviors. It only states it 'gets' information, implying a non-destructive read, but omits idempotency, auth requirements, or side effects. More detail is needed for safe agent use.
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 single-sentence description is concise with no fluff, earning base credit. However, it lacks structure or front-loading of key usage info, making it adequate but not well-organized.
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?
For a simple tool with one enum parameter and no output schema, the description is too minimal. It fails to specify the nature of the 'configuration and recommendations,' leaving an agent uncertain about the return value and how to interpret results.
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%, so the description should explain parameter meanings beyond the self-explanatory enum values. It merely mentions 'stage' implicitly, adding no insight into what configuration or recommendations are returned for each stage.
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 retrieves configuration and recommendations for a specific stage, which is a distinct read operation. The verb 'get' and resource 'configuration and recommendations' are specific, and the enum of stages differentiates it from sibling tools that create, save, or report.
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 siblings like get_model_recommendation or get_recommended_prompt. It lacks context on prerequisites or alternative tools for similar tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It fails to disclose side effects, idempotency, authentication needs, or what 'clean output' entails (e.g., format, 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?
Single sentence with no filler. Parenthetical clarification 'not full chat' adds essential context efficiently. Perfectly concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 2 required params, no output schema, and no annotations, the description is severely incomplete. It omits return format, preconditions, and what 'clean output' means for downstream use.
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 description coverage is 0%. Description does not explain what 'ticket_id' or 'stage' represent, nor acceptable values or formats. Both parameters are undocumented beyond the schema.
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?
Description uses specific verb 'load' and resource 'clean output from previous stage', clearly distinguishing from full chat. However, it does not explicitly differentiate from sibling tools like create_session_checkpoint or get_stage_config.
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. The 'not full chat' hint is implicit but insufficient for deciding between nine sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, placing the full burden on the description. It only indicates a mutate action (reporting) but omits critical behavioral details such as side effects, idempotency, required permissions, or what happens after reporting.
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 a single sentence of 7 words, which is concise but too brief for a tool with 7 parameters and no other documentation. It says nothing about how to structure input or expected output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 parameters, no output schema, no annotations), the description is grossly incomplete. It fails to convey any behavioral context, parameter usage, or return expectations, leaving the agent with little confidence.
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?
With 0% schema description coverage, the description must compensate but does not add any meaning beyond parameter names. The schema has 7 parameters (6 required), yet the description merely states the tool's purpose without explaining fields like 'title', 'severity', or 'related_test_case_ids'.
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 'Report', the resource 'a new defect/bug', and the context 'during testing'. It is specific and differentiates from sibling tools like 'record_test_result' which likely focus on test outcomes.
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 (e.g., 'record_test_result' or 'create_test_case'). There is no mention of prerequisites, exclusions, or usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It only states creation but omits side effects, permissions, or whether it overwrites existing data. No behavioral context beyond the verb.
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 a single concise sentence with no redundancy. However, it omits important parameter details, which would justify a slightly lower score.
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 6 parameters, no output schema, and no annotations, the description fails to provide enough context for an agent to invoke the tool correctly without external knowledge.
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 description only lists four of six parameters (missing tags and priority). It adds no detail on types, formats, or constraints beyond what is in the schema.
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 action 'Create' and the resource 'test case', listing key fields. However, it does not differentiate from sibling tools like 'save_test_cases' or 'find_reusable_test_cases', though the task is distinct.
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 such as 'save_test_cases' or 'report_defect'. No prerequisites or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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. The minimal description ('Get the recommended prompt structure for a specific stage') does not disclose any behavioral traits, such as whether the tool is read-only, requires authorization, or what the response contains. This is insufficient for a tool with zero 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, making it very concise. However, it sacrifices informativeness for brevity. While every sentence earns its place, the content is largely redundant with the tool name and schema. A fully informative description would be slightly longer.
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 there are no annotations, no output schema, and only one parameter, the description is too minimal. It fails to explain what the prompt structure is used for, what the return value looks like, or any additional context needed for correct invocation. The tool is part of a suite of QA tools, but the description does not leverage that context.
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 0%, so the description adds no parameter details. However, the input schema itself is well-defined with a single required parameter 'stage' and an enum of six clearly named stages (e.g., 'ticket-analysis', 'test-planning'), which are self-explanatory. The description does not add extra meaning, but the schema compensates somewhat. A score of 3 reflects that no additional value is provided beyond the schema.
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 states 'Get the recommended prompt structure for a specific stage', which clearly identifies the verb ('Get') and the resource ('recommended prompt structure'). It distinguishes from siblings like 'get_stage_config' and 'get_model_recommendation' by focusing on prompt structure. However, it lacks specificity about what the prompt is used for, especially given the QA context.
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 is provided on when to use this tool versus alternatives. There is no explicit context, exclusions, or mention of prerequisites. The description is too terse to help an agent decide between this and sibling tools like 'get_stage_config' or 'get_model_recommendation'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It only states 'save a clean output' without disclosing whether it overwrites, requires specific permissions, or what happens on conflict. For a mutation tool, this is insufficient.
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?
Single sentence is concise but underinstructive for a tool with 6 parameters. Front-loads the core purpose but lacks structure like grouping or parameter explanations.
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?
Tool has 6 parameters, no output schema, and no annotations. Description fails to explain return value, error handling, or the meaning of 'clean output'. Incomplete for reliable agent invocation.
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 coverage is 0% and description adds no meaning to any of the 6 parameters (ticket_id, stage, output, tokens_used, time_spent_seconds, output_quality). Agent gets no help understanding what each field means or how to fill 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 the verb 'save' and resource 'output' with context 'at the end of each stage for next stage input'. It distinguishes from siblings like 'load_stage_checkpoint' which loads rather than saves.
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?
Implies use at end of stages but provides no explicit guidance on when to use versus alternatives like 'create_handoff_summary' or when not to use. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description must disclose behavior. It only hints at a mapping (analysis vs execution) but omits details like output format, side effects, or whether the recommendation is static. An agent cannot infer response structure or caching 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 a single sentence with no wasted words. However, it omits critical details that would make it maximally useful, sacrificing completeness for brevity.
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 low schema coverage, no output schema, and no annotations, the description is insufficient. It fails to explain return values, the exact model for each stage, or edge cases. An agent would need to infer or test to use it reliably.
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%, so the description should explain parameter semantics. It mentions 'Sonnet for analysis, Haiku for execution' but does not map individual stage enum values to analysis or execution, leaving ambiguity (e.g., is 'test-case-generation' analysis or execution?). The description adds minimal value over the enum list.
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: 'Get recommended model' with a specific verb and resource. It distinguishes from sibling tools like 'get_recommended_prompt' by explicitly focusing on model selection (Sonnet vs Haiku) for different QA stages.
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 is provided on when to use this tool versus alternatives such as 'get_recommended_prompt' or 'get_stage_config'. The description does not specify prerequisites or typical call contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states only that the tool generates a report with 'all metrics and statistics', but does not disclose behavioral traits such as whether it modifies data, requires authentication, or has performance implications. For a report generator, read-only nature is implied but not explicit.
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 a single, concise sentence with no superfluous words. It is front-loaded with the key action and resource, making it quickly scannable. Every part earns its place.
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 zero parameters and no output schema, the description is minimally complete but lacks detail on what 'comprehensive' entails or the output format. Sibling tools suggest a testing context, but the description does not connect to the broader workflow or specify if the report is interactive or a file.
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?
There are no parameters, so schema coverage is 100% and baseline is 4. The description adds meaning beyond the schema by specifying 'all metrics and statistics', which clarifies the output scope. No further parameter detail is needed.
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 verb 'generate' and the resource 'QA report', indicating a distinct purpose. It is not a tautology and distinguishes from sibling tools that focus on individual test cases or defects, though it could be more specific about the report scope.
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. The description does not mention conditions, prerequisites, or scenarios where it is appropriate. It fails to differentiate from other tools like 'validate_qa_output' or 'create_handoff_summary'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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