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 implies generative behavior ('gera diagnóstico e testes compiláveis') but does not specify side effects like file creation, authentication requirements, or whether the tool is read-only or mutable.

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

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

The description is a single sentence that conveys key information (pipeline version, analysis, pattern detection, output generation) without redundancy. It is front-loaded with the primary action, though mixing Portuguese and English may reduce clarity for some agents.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description should explain what the tool returns. It mentions generating diagnosis and tests but does not specify the format or how the orchestrator agent presents them. The description is adequate for a simple analysis tool but lacks detail on response structure.

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

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

Schema coverage is 100% with both parameters described ('Pergunta ou descrição do problema' and 'Código-fonte Java/Kotlin'). The tool description does not add additional meaning beyond what the schema already provides, so a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it analyzes code, detects GPA patterns, and generates diagnosis and compilable tests via OrchestratorAgent. It provides a specific verb and resource, but lacks explicit differentiation from sibling tools like 'analyze_test_coverage' or 'diagnose_test_failure', which have narrower scopes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No guidance is given on when to use this tool versus alternatives such as 'analyze_test_coverage' or 'multi_agent_analyze'. The description does not provide context for selection or exclusions.

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

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

No annotations are provided, so description must carry full burden. It describes the tool as analyzing a report and producing gaps/suggestions, implying read-only behavior. However, it does not explicitly state it is non-destructive, nor does it mention authorization needs or side effects. The description is adequate but could be more transparent about behavior.

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

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

The description is two sentences, front-loaded with the main action. It is concise and avoids fluff. Could be slightly more compact, but it efficiently conveys the tool's purpose and outputs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, all described, no output schema), the description covers the main actions: analyzing coverage, identifying gaps, and suggesting tests. It hints at output but does not fully describe the return format. Still, it is mostly complete for a coverage analysis tool.

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

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

Schema description coverage is 100%, so all parameters have descriptions. The overall description does not add new meaning beyond the schema (e.g., it mentions 'módulo ou serviço' which aligns with the schema's 'Nome do microsserviço ou módulo'). No extra parameter guidance is provided, so baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool analyzes test coverage of a specific module/service, identifies gaps and uncovered lines, and suggests new test cases. This distinguishes it from siblings like 'generate_test_suite' (generates tests) and 'diagnose_test_failure' (diagnoses failures). The verb+resource is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

The description implies usage when analyzing test coverage, but does not explicitly state when not to use or name alternatives. However, the context is clear enough that an agent would use this for coverage analysis rather than other tasks like generating tests or diagnosing failures.

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

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

With no annotations provided, the description must disclose behavioral traits. It only lists high-level outputs but does not mention side effects (e.g., if it modifies any state), required permissions, rate limits, or what happens with invalid inputs. The description lacks transparency about the tool's behavior beyond its result.

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

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

The description is a single sentence that efficiently conveys the core functionality. However, it is in Portuguese while the tool name is English, which may reduce clarity for some agents. Otherwise, it is concisely structured with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description should provide more context about the return format, whether the fix is a suggestion or code, and how the inputs are used. It leaves important details for the agent to infer, making it incomplete.

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

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

The input schema has 100% description coverage, so each parameter's purpose is already clear. The description adds 'root cause, type, fix' but doesn't directly map to parameters, providing marginal additional value. Baseline 3 is appropriate given high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Diagnoses failure in existing test: identifies root cause, classifies type (flaky, regression, environment) and suggests fix.' It specifies the resource (test failure), the action (diagnose), and the outputs (root cause, type, fix), making it distinct from sibling tools like 'analyze' or 'generate_test_suite'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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, such as the async sibling 'task_diagnose_failure_async'. It does not mention prerequisites, typical scenarios, or exclusions, leaving the agent without context for selection.

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

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

The description explains the generated workflow's behavior, but does not disclose the tool's own behavior: e.g., whether it overwrites files, requires permissions, or has side effects. No annotations are provided, so the description carries the full burden but is only partially transparent.

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

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

A single sentence that is front-loaded with the main purpose and includes key details. Every word earns its place; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description sufficiently explains what the tool produces (a GitHub Actions workflow) and its steps. It lacks details on output format (e.g., YAML) and prerequisites, but given the tool's simplicity and no output schema, it covers most contextual needs.

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

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

Schema coverage is 100% with all parameters described. The tool description does not add deeper meaning beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates a complete GitHub Actions workflow for coverage analysis in pull requests, listing specific steps (checkout, build, JaCoCo, merge block, PR comment). It is distinct from siblings like analyze_test_coverage (analyzes existing coverage) and generate_test_suite (generates tests, not pipelines).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

The description implies usage for setting up CI coverage checks, but it does not explicitly state when to use this tool vs alternatives (e.g., analyze_test_coverage for existing reports). No exclusions or alternative tool references are mentioned.

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

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

No annotations exist, so the description must carry the full burden. It only states the generation action but does not disclose side effects, permissions needed, output format, or whether it modifies files. This lack of detail reduces transparency.

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

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

The description is a single, front-loaded sentence that conveys the core purpose efficiently. It has no wasted words, but could be slightly more structured (e.g., bullet points for doc types).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and three parameters, the description is adequate but not thorough. It lacks details on output behavior, such as whether documentation is returned or saved, and does not clarify the full scope of generation (e.g., full vs. snippet).

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

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

Schema coverage is 100%, so the schema already describes all parameters. The description adds minimal extra meaning beyond listing some doc types, which are already enumerated in the schema's enum. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates technical documentation from code and lists specific documentation types (JSDoc, Javadoc, etc.). It is distinct from sibling tools that focus on analysis, testing, or architecture mapping.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No guidance is provided on when to use or avoid this tool. There are no sibling tools with similar functionality, so the description misses an opportunity to clarify usage context or prerequisites.

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

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

No annotations provided, so the description must disclose behavior. It claims to generate a 'complete' suite for 100% coverage but does not clarify side effects (file creation, mutability), return format, or limitations. This is insufficient for a generation tool.

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

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

Single sentence with no wasted words, front-loaded with the core purpose. It could be slightly more structured but is efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite 4 parameters and no output schema, the description fails to address return values, file generation behavior, or edge cases like unmet coverage. The tool's complexity demands more detail.

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

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

Schema coverage is 100%, and the description adds some context (e.g., method/class for code_snippet, pre-identified gaps for coverage_gaps). However, the additions are minimal; the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates a complete test suite (unit, integration, contract) for 100% coverage, with a specific verb and resource. It distinguishes from siblings like analyze_test_coverage and generate_cicd_pipeline.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No explicit guidance on when to use or when not. Alternatives like task_generate_test_suite exist but are not mentioned. The description does not differentiate from analysis tools or async versions.

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

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

No annotations provided, so description must cover behavior. It correctly implies read-only ('Exibe'), but does not disclose potential performance impacts, authorization needs, or data freshness. The list of metrics adds some transparency.

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

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

Two sentences, no extraneous words. Front-loaded with main verb ('Exibe') and resource ('métricas de evolução'). Every word serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

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 adequately conveys what the tool returns. Lacks details on authentication, caching, or data scope but is sufficient for a simple read operation.

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

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

No parameters exist; schema coverage is 100%. Description compensates by fully explaining the output (metrics shown). Exceeds baseline of 3 by providing meaningful return value details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool displays system evolution metrics, listing specific items (total interactions, quality score, trend, individual performance). Distinguishes from sibling tools like 'analyze' or 'generate_documentation' which have different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No explicit guidance on when to use this tool vs alternatives. However, the purpose is clear enough that an agent would infer usage for retrieving system metrics, but lacks exclusions or context-specific advice.

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

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

The description implies a read-only list operation, but with no annotations, it does not explicitly state safety or non-destructiveness. Minimal but sufficient.

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

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

One concise sentence, front-loaded with purpose, no extraneous detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no output schema, and description does not explain return format. It mentions 'description and input schema' for tasks, but omits output structure. Adequate but incomplete.

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

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

No parameters exist, so schema coverage is 100% trivially. Baseline score 4 applies; description adds no parameter info but none is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'list' and resource 'all available tasks', which is distinct from sibling tools like 'run_task' or 'analyze'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 whether to use 'list_tasks' before other task-related tools.

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

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

With no annotations, the description carries full burden but only states high-level actions. It does not disclose whether the tool is read-only, requires special permissions, has side effects, or any limitations (e.g., output format, performance).

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

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

Single sentence is concise and front-loaded with key actions. No redundant text, though could be improved by structuring or adding bullet points for clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lacks information about the tool's output format or return value. Given no output schema and 3 parameters, the agent needs to know what to expect after invocation (e.g., a list, a report, or a map). This is a notable gap.

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

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

Schema coverage is 100% with parameter descriptions already provided. The tool description adds no further semantic information about parameters, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states three specific actions: mapping dependencies, identifying failure points, and suggesting test strategies. It distinguishes itself from sibling tools like 'analyze' (vague) and 'generate_test_suite' (narrower focus) by being explicitly about decentralized architecture mapping.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No explicit guidance on when to use this tool vs. alternatives (e.g., analyze, task_map_architecture_async). The context is implied but not stated, and there are 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.

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

Since no annotations exist, the description carries the full burden. It discloses key behaviors: orchestrating agents in real time, pattern detection, generation of tests and Javadoc, and persistence to Knowledge Base. This makes the tool's write side effects clear, though it does not detail safety or reversibility.

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

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

The description is a single dense paragraph, front-loaded with the core concept. Every sentence contributes specific information about the pipeline stages and outputs. It is appropriately concise for the complexity, though a more structured layout could improve scannability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multi-agent, multiple outputs) and lack of output schema, the description provides a high-level overview but omits detailed return structure. It mentions generated outputs but not their format (e.g., files, strings). For an AI agent, this may be insufficient to fully understand the tool's response.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal meaning beyond the schema—it mentions analyzing code snippets and intents but does not provide formatting or constraints not already in the schema. No added value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a complete multi-agent pipeline for real-time orchestration, detecting patterns, generating tests, documentation, and persisting knowledge. It uses specific verbs (orchestrates, detects, generates, persists) and resources (ANALYST, TESTER, etc.), and distinguishes itself from siblings like analyze or generate_test_suite by combining multiple steps.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

The description does not provide explicit guidance on when to use this tool vs alternatives (e.g., simpler analyze or generate_test_suite). It lacks 'when not to use' or references to sibling tools, leaving the agent to infer based on the description of comprehensiveness.

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

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

With no annotations, the description carries the transparency burden. It discloses the matching approach (fuzzy keyword), return count (top-3 default), and returned fields. It does not explicitly state it is read-only, but the query nature implies no side effects. This is sufficient for a simple query tool.

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

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

The description is three concise sentences, each carrying essential information. It front-loads the main purpose and efficiently covers behavior and output. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple query tool with full parameter schema, the description is mostly complete. It explains what the tool does and what it returns. However, it could be slightly more explicit about the output format (e.g., list of objects with the specified fields) since no output schema exists.

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

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 no new parameter information beyond what the schema provides, but it reinforces the default for top_n. No additional meaning or constraints are contributed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: consulting a knowledge base to find similar solutions using fuzzy keyword matching. It specifies the resource, the matching method, and the return fields (confidence, reuse count). This distinguishes it from sibling tools like 'show_knowledge' which likely displays all entries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

The description implies usage when needing to find similar solutions, but lacks explicit guidance on when to use this tool versus alternatives. No exclusion criteria or comparative direction is provided.

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

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

No annotations provided, so description should fully cover behavioral traits. It mentions outputs but not side effects, permissions, rate limits, or whether the tool is read-only. This is minimal disclosure for a reverse engineering tool.

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

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

Description is a single concise sentence with no fluff. It could be more structured (e.g., bullet points) but remains efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Lacks completeness: no output schema, no information on return format, code size limits, or language-specific nuances. For a complex analysis tool, more context is needed.

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

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

Schema description coverage is 100%, and descriptions are clear. The tool description adds no extra parameter meaning beyond what's already in the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool analyzes undocumented code and generates specific outputs (functional description, contracts, dependencies, risks). The verb 'analisa' and resource 'código' are specific, and it distinguishes from siblings like 'analyze' which is generic.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 like 'task_reverse_engineer' or 'analyze'. The description implies usage for undocumented code but lacks explicit context or exclusions.

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

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

With no annotations, the description must disclose behavior. It only says 'Executa' (executes), but does not state whether execution is synchronous or asynchronous, error handling, side effects, or return format. The presence of async sibling tools suggests this may be synchronous, but it's 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.

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

The description is very short, but includes an unnecessary version label ('v4.0') at the start. It could be more concise by removing the version and leading with the core function. However, it is not overly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of a multi-task executor with no output schema, the description is insufficient. It lacks information about return values, sync/async behavior, error states, and how the 'input' parameter interacts with each task. The sibling tools provide context but the description itself does not.

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

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

Schema coverage is 100%, so the baseline is 3. The description lists the task_id values already defined in the schema enum, adding no new meaning. The 'input' parameter is described only generically in the schema, and the description does not clarify its structure or provide examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states that the tool executes a specific task from a list of tasks, which is accurate. However, it does not distinguish itself from sibling tools that are individual task tools or async versions, leaving ambiguity about when to use this generic runner.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 the sibling tools that perform specific tasks (e.g., 'analyze', 'generate_test_suite'). It does not mention alternatives or prerequisites, leaving the agent without decision criteria.

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

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

No annotations are provided, so the description must fully convey behavior. The description only says 'displays', implying read-only, but omits details about side effects, permissions, or what happens when the optional category parameter is omitted.

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

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

The description is a single sentence, including a version number. It is concise and front-loaded with the main purpose, though the version number is unnecessary and could be replaced with more useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has one optional parameter, no annotations, and no output schema, the description lacks completeness. It does not explain the output format, behavior when no category is provided, or how it differs from related tools like query_knowledge_base.

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

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

Schema description coverage is 100% for the single parameter 'category', which includes an enum. The parameter description 'Categoria a visualizar' adds minimal meaning beyond the schema. The description text lists some categories but misses 'services', creating slight inconsistency.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool displays metrics and data from the KnowledgeBase, listing specific categories (patterns, solutions, quality metrics). However, it does not differentiate itself from sibling tool 'query_knowledge_base', which likely has a similar purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 like query_knowledge_base. There is no indication of prerequisites, context, or exclusions.

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

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 indicates the tool is asynchronous and provides real-time progress, but does not detail side effects, permissions required, or how progress is tracked (e.g., polling, callbacks). The lack of safety information (e.g., read-only vs destructive) is a gap.

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

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

The description is two sentences long and front-loaded with the core action. It is concise and avoids redundancy, though the first sentence could be more informative than a near-repetition of the title.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's async nature, lack of output schema, and three parameters, the description is insufficient. It does not explain the output format, how to retrieve results, or potential side effects. Real-time progress is mentioned but not detailed. The description seems tailored for Portuguese-speaking users but lacks completeness for an automated agent.

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

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

The input schema has 100% coverage with descriptions in Portuguese. The description adds that the analysis identifies gaps and generates suggestions, which is not in the schema but provides context on output. However, since the schema already explains each parameter's meaning, the description adds minimal value beyond baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs an asynchronous analysis of test coverage with real-time progress. It mentions identifying critical gaps and generating suggestions, which adds specificity. However, it does not explicitly differentiate itself from similar sibling tools like analyze_test_coverage or task_analyze_full_coverage, though the async aspect is implied as distinctive.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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. It implies usage for async analysis with progress, but does not mention prerequisites, limitations, or scenarios where other tools (e.g., synchronous analysis) would be more appropriate.

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

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

No annotations are provided, so the description carries full burden. It mentions real-time progress but does not disclose behavioral traits like destructiveness, read-only, auth requirements, or side effects. The description provides minimal behavioral insight.

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

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

Single sentence is concise and front-loaded. However, it is in Portuguese and could include more specifics without significant length increase.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Lacks explanation of return values, pagination, or behavior (real-time progress not elaborated). With no output schema and minimal description, completeness is low for an analysis tool.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds no additional meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the resource ('coverage') and action ('analyze'), and adds real-time progress. However, it does not differentiate from sibling tools like 'analyze_test_coverage' or 'task_analyze_coverage_async'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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. No prerequisites or context provided. The description only states what it does.

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

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

No annotations provided, so description carries full burden. It states 'async' but does not explain the mechanism (e.g., returns a job ID, requires polling). No disclosure of side effects, permissions, or idempotency.

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

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

Very concise (two sentences) with front-loaded key information. Could include structure like usage context, but efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema. Description lacks details on async return type, how to retrieve results, or how corrections are suggested. Incomplete for a tool with three parameters and no annotations.

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

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

Schema coverage is 100%, so baseline is 3. The description does not add additional meaning to the parameters beyond what the schema descriptions already provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: asynchronous diagnosis of test failures by analyzing stack traces and suggesting corrections. It specifies async, distinguishing it from a likely synchronous sibling 'diagnose_test_failure'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No explicit guidance on when to use this tool versus alternatives like 'diagnose_test_failure' (sync) or 'analyze'. No conditions or prerequisites mentioned.

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

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

The description is limited to the stated purpose. It does not disclose side effects, rate limits, required permissions, or how the asynchronous result is obtained. No annotations are present to fill the gap.

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

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

The description is very concise, single sentence, and directly states the tool's purpose. It is efficient but could benefit from additional structuring to highlight key aspects like async behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description lacks context on the return value, how to handle the async response, and prerequisites. It is insufficient for an agent to use the tool correctly without further information.

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

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

The input schema covers all parameters with descriptions. The description adds no additional semantic value beyond what the schema provides, such as hinting at mock strategy relevance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: asynchronous generation of a complete test suite including happy path, edge cases, and mocks. It distinguishes itself from sibling tools like 'generate_test_suite' by emphasizing the async nature.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 mention of prerequisites, when to prefer synchronous generation, or when to use other sibling tools like 'task_generate_test_suite'.

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

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

With no annotations, the description carries the full burden of disclosing behavior. It mentions real-time progress, which is a useful behavioral trait, but fails to specify whether the tool is read-only or modifies data, its side effects, or any required permissions. Key behavioral aspects are omitted.

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

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

The description is a single concise sentence that front-loads the core purpose and a key feature (real-time progress). There is no wasted text, though additional details could be included without harming conciseness. It earns a 4 for being appropriately sized and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has three parameters and no output schema, so the description should explain the output or result of generation. It only mentions real-time progress but not what the tool returns or produces. Given the complexity and lack of output schema, this is a significant gap, resulting in incomplete context.

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

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

Schema description coverage is 100%, so the baseline is 3. The descriptions of parameters (code_snippet, framework, mock_strategy) are minimal and essentially repeat the parameter names without adding substantive meaning or usage hints. The tool description itself does not compensate by elaborating on parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (generate) and resource (test suite), and adds a distinguishing feature (real-time progress). However, it does not differentiate from the sibling tool 'generate_test_suite', which likely performs a similar synchronous operation, leaving ambiguity about when this specific tool should be chosen.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 like 'generate_test_suite' or 'task_generate_tests_async'. No context is given about prerequisites, required inputs, or scenarios where this tool is preferred.

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

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It only states the tool's purpose (identifying SPOFs, etc.) but does not reveal any behavioral traits such as side effects, authorization needs, rate limits, or whether it modifies any state.

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

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

The description is a single sentence that front-loads the core purpose. It is concise with no filler, though it could benefit from slightly more structure to include usage hints.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple (2 params, no output schema), but the description does not mention return values or output format. It lacks completeness in setting expectations for what the agent will receive after invocation.

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

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

The input schema already describes both parameters completely (100% coverage). The description adds no parameter-specific information, so it does not exceed the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: asynchronous architecture mapping that identifies SPOFs, critical dependencies, and resilience. This goes beyond the name and title, providing specific outcomes that differentiate it from general mapping tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 'map_decentralized_architecture' or other analysis tools. It does not mention prerequisites, limitations, or alternative scenarios.

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

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

No annotations are provided, so the description carries the burden. It discloses the async nature and polling mechanism ('com polling de status'), but lacks details on failure handling, rate limits, or how to obtain results beyond polling. The transparency is adequate but not thorough.

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

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

The description is a single, efficient sentence that includes all essential information: the pipeline steps, async nature, and polling mechanism. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex async orchestrator with 2 parameters and no output schema, the description covers the pipeline steps and polling. It could mention that it returns a task ID for polling, but the polling reference is sufficient. Slightly incomplete but mostly adequate.

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

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

Schema coverage is 100% with descriptions for both parameters. The tool description adds high-level context (executes pipeline) but no additional parameter-specific details. Following the baseline for high coverage, a score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool executes the complete Multi-Agent pipeline (ANALYST → TESTER → ARCHITECT → DOC → MEMORY) as an async task, using a specific verb and resource. It distinguishes itself from sibling tools like task_generate_tests_async which handle only sub-parts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

The description implies usage for full pipeline orchestration but does not explicitly state when not to use it or name alternatives. However, the sibling tool list contains many specific task tools, so the agent can infer this is for comprehensive analysis.

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

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

The description mentions 'real-time progress', which hints at a streaming or long-running behavior. With no annotations, this adds some value, but it does not disclose other traits like whether the tool is read-only, destructive, or what happens with large inputs.

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

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

The description is a single, focused sentence that conveys the core purpose and a key feature. No unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description lacks information about return values or output format. It also does not mention how the tool integrates with the module context or what the 'reverse engineering' produces (e.g., diagram, analysis). The 3 parameters are described in schema, but completeness suffers from missing output details.

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

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

Schema coverage is 100% with parameter descriptions provided. The description adds no additional meaning beyond the schema, so it meets the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs reverse engineering of a module with real-time progress. The verb 'reverse engineer' and resource 'module' are explicit. However, it does not differentiate from siblings like 'reverse_engineer_module' or 'task_reverse_engineer_async', which could cause confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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 alternatives. There is no mention of prerequisites, typical scenarios, or exclusions, leaving the agent without context for selection.

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

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

No annotations provided, so the description must fully disclose behavior. It mentions 'async' which is already in the name, but does not explain side effects, idempotency, rate limits, or return type. The description adds minimal value beyond the tool name.

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

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

Single sentence succinctly states the tool's purpose. It is front-loaded with the key action. However, being too short, it omits important details about async behavior and output, which slightly reduces effectiveness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an async tool, the description fails to explain what the tool returns (e.g., task ID, progress URL). No output schema exists, so the description should describe the return value. It mentions extracting contracts and generating docs but does not clarify how the agent gets these results.

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

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

Input schema has 100% coverage with descriptions for all 3 parameters. The tool description repeats what is already in the schema (e.g., code snippet, language, context) without adding new semantic meaning or usage hints. Baseline 3 is appropriate since schema handles parameters well.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it is an asynchronous reverse engineering tool for modules, extracting contracts, dependencies, and generating documentation. It distinguishes from siblings like reverse_engineer_module and task_reverse_engineer by adding 'async' in name and description, but does not elaborate on what asynchronous means in practice.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

No guidance on when to use this async version versus the synchronous alternatives. The description does not mention use cases, prerequisites, or situations where this tool is preferred.

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