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

With no annotations provided, the description carries the full burden. It mentions '[mode: real]' and auto-detection of platform, which are useful behavioral traits, but it does not state whether the tool is read-only, what it returns, or any side effects. Given the lack of annotations and safety disclosure, 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.

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

The description is a single sentence, efficient and free of fluff. It packs relevant information about the tool's mode and content. It is slightly under-specified, but it does not waste any words, earning a strong score.

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 moderately complex with 3 parameters, no annotations, and no output schema. The description covers only the core purpose and one parameter behavior, omitting details about depth levels, expected inputs, output format, and any limitations. This is not complete enough for an AI agent to confidently invoke the 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?

The schema description coverage is 0%, so the description must explain parameters. It only addresses 'platform' by noting auto-detection when not provided, leaving 'depth' and 'projectPath' unexplained. This does not adequately compensate for the missing schema descriptions.

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 static analysis of a project, listing specific aspects like screens, components, APIs, and state. This is a specific verb-resource combination. However, it does not explicitly differentiate from sibling tools such as analyze_code_deep or diagnose_project, so it misses the top score.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or scenarios where another tool would be more appropriate. The only hint is 'static analysis,' which is implied rather than explicit.

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 carries full burden for behavioral disclosure. It states the tool performs deep AST analysis but does not mention whether it is read-only, what side effects it has, what 'mode: real' means, or what the output looks like. This is insufficient for an agent to predict tool 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 a single sentence, making it highly concise and front-loaded with the core purpose 'Deep AST analysis'. However, the '[mode: real]' prefix is unexplained and adds ambiguity without earning its place.

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 no output schema and no annotations, yet the description does not explain what results are returned, how this deep analysis relates to other analysis tools, or what 'hooks' and 'issues' specifically refer to. The description is too sparse for a tool with this complexity.

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 0%, and the description does not mention either parameter. 'platform' and 'projectPath' are somewhat self-explanatory by name, but the description fails to clarify how 'platform' affects the analysis or why it is optional. The tool description provides no semantic enrichment beyond the schema.

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 identifies the tool as performing deep AST analysis and enumerates analysis targets (functions, complexity, hooks, issues). This goes beyond a tautology of the tool name and distinguishes it from siblings like analyze_app_structure, though the meaning of 'issues' is vague.

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 such as analyze_app_structure or detect_logic_errors. The cryptic '[mode: real]' prefix hints at a mode distinction but is not explained, leaving the agent without clear selection 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?

With no annotations provided, the description carries the full burden of disclosing behavior. It states the analysis is 'static', hinting at non-destructive read-only behavior, but it does not disclose whether it modifies any state, what inputs are required, or what the output format is. The description is too terse to provide adequate 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.

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

The description is a single sentence with minimal waste, and the key purpose is front-loaded. However, the '[mode: real]' prefix adds a small amount of irrelevant metadata that doesn't help comprehension. Overall, it's concise but not a model of informative brevity.

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 annotations, no output schema, and only two parameters, the description still fails to convey essential operational details such as expected inputs, return value, or usage context. The tool is an analyzer, but an agent cannot determine what to pass or what to expect back, making the description insufficient for reliable 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 schema has 0% description coverage, and the description does not mention projectPath or platform at all. It provides no hints about how these parameters affect the analysis, leaving the agent to infer from the schema alone. The described focus areas (rendering, computation, bundle) give vague context but no concrete parameter mapping.

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 identifies the tool as performing static performance analysis, specifying three focus areas (rendering, computation, bundle). This distinguishes it from sibling analysis tools like analyze_app_structure or detect_memory_leaks, though it doesn't name them explicitly. The verb-resource pairing is specific and clear.

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 word 'static' implies using this tool when a non-runtime analysis is needed, contrasting with dynamic tools like run_simulation or run_stress_test. However, it provides no explicit when-to-use guidance or alternatives, leaving usage to be inferred from the term 'static' alone.

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 that the tool operates in 'real' mode, that backup and validate default to true, and that dryRun is supported. These are useful behavioral traits beyond the name, though it does not describe error handling or exact consequences of applying the fix.

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 extremely concise, with three short sentences that front-load the purpose and then provide key behavioral details. No wasted words, and the structure is logical: purpose first, then defaults and mode.

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 4 parameters, no output schema, and no annotations, requiring the description to cover usage, parameters, and expected outcomes. The description is too brief, omitting explicit alternatives, parameter details, and any information about return values or side effects, making it incomplete for effective tool selection and 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?

Schema description coverage is 0%, so the description must explain parameters, but it only mentions defaults for backup and validate and the existence of dryRun. It does not explain the meaning or valid values for fixId, backup, validate, or dryRun, leaving parameter semantics largely undefined.

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: 'Apply a confirmed fix.' It uses a specific verb ('apply') and resource ('fix'), and the context of 'confirmed' distinguishes it from sibling tools like confirm_fix, suggest_fixes, and rollback_fix.

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 after a fix is confirmed, but does not explicitly state when to use this tool versus alternatives. It mentions dryRun support but does not say when to use dryRun or when to prefer other tools, leaving usage guidance implied rather than explicit.

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. It mentions 'mode: real' and 'prior to apply' but does not disclose side effects, required permissions, or the state changes for each action (approve/reject/modify). This is insufficient for a mutation 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 a single concise sentence that is easy to parse, but over-conciseness sacrifices necessary detail. It earns a 4 for efficiency, though it could be restructured to include more guidance without bloating.

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 4 parameters, no annotations, and no output schema, the description is severely under-specified. It offers only a high-level purpose and does not cover per-action requirements, side effects, or return values, making it inadequate for reliable tool 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?

Schema description coverage is 0%, so the description must compensate. It only maps 'confirm/reject/modify' to the action enum but does not explain fixId, reason, or modifiedCode, nor when they are required (e.g., modifiedCode for 'modify'). This leaves agents to guess parameter behavior.

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 with specific verbs: confirm, reject, or modify a proposed fix. It distinguishes from siblings like 'apply_fix' by explicitly noting 'prior to apply'.

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 phrase 'prior to apply' implies the tool's position in the workflow, but it does not explicitly name alternatives or say when not to use it. The context is clear enough for an agent to infer the pre-apply usage.

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 carries the full burden for behavioral disclosure. It indicates the tool creates a test plan but does not explain side effects, permissions, or the meaning of the '[mode: real]' prefix. It is unclear whether it writes to disk or affects stored scenarios.

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, compact sentence with no wasted words, and the '[mode: real]' prefix is front-loaded. While concise, it is exceptionally terse, but brevity itself is not penalized; it is appropriate for a simple tool description.

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 6 parameters, 2 required, no output schema, and no annotations. The description provides only a high-level purpose and does not explain required parameters, filter semantics, scheduling behavior, or return values. This is inadequate for an agent to invoke the tool correctly without additional schema inference.

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 description provides almost no parameter-level detail. It hints at 'filtering' but does not map this to the typeFilter or priorityFilter parameters, and 'scheduling' does not clearly correspond to any schema parameter. With 0% schema description coverage, the description fails to explain any of the six parameters beyond a vague conceptual link.

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 builds a test plan from stored scenarios, using a specific verb ('Build') and resource ('test plan'). It also distinguishes from sibling tools by indicating it operates on existing scenarios rather than generating or executing them.

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 the tool is for creating plans from existing scenarios and mentions filtering/scheduling, but does not explicitly state when to use it over alternatives like run_scenario_test or generate_scenarios. No exclusions or alternative tool references are 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?

With no annotations provided, the description carries the full burden for behavioral disclosure. It does not state whether the tool is read-only, requires any permissions, or how it interacts with the project. The '[mode: real]' prefix is ambiguous and does not explain expected behavior, side effects, or output.

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 short sentence, which is concise, but it is not well-structured. The leading '[mode: real]' is cryptic and unexplained, and the sentence lacks a clear subject or context. It front-loads an obscure qualifier rather than a clear purpose statement.

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 3 parameters with no schema descriptions, no annotations, and no output schema, the description should explain inputs, return values, and behavioral context. It only lists a few error types, omitting essential information like projectPath requirements, analysisDepth semantics, and what the tool returns. This is a significant 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?

The schema has 0% description coverage, so the description must compensate. While the listed error types (race conditions, null refs, state inconsistencies) map to the checkTypes enum values, it does not explain the projectPath or analysisDepth parameters. The description adds only marginal meaning for one parameter and leaves the others unexplained.

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 detects race conditions, null refs, and state inconsistencies, which are specific logic error types. It uses an active verb and names concrete targets, making the purpose understandable. However, it does not distinguish from the sibling tool 'detect_race_conditions', which also detects race conditions, causing potential 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 alternatives. It does not mention overlapping tools like detect_race_conditions or detect_security_issues, nor does it indicate scenarios where this tool is preferred. The phrase '[mode: real]' hints at a mode but does not clarify usage context.

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 '[mode: real]' hint offers a small behavioral clue, but with no annotations the description carries the full burden. It does not disclose side effects, whether it modifies files, requires running the app, or any permissions needed, leaving significant behavioral uncertainty.

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, concise sentence that directly states the tool's function. No fluff or redundant information, every word contributes to the core message.

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 three parameters, zero schema descriptions, no annotations, and no output schema, the description is far from complete. It does not explain parameter semantics, output format, or operational context, making it inadequate for correct 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?

Schema description coverage is 0%, and the description adds no meaning to the three parameters (projectPath, analysisType, minLeakSizeMB). An agent cannot infer what values to pass, especially for analysisType and minLeakSizeMB, without external knowledge.

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 uses a specific verb 'Detect' and names precise resource types ('memory leaks, retain cycles, unclosed resources'), clearly distinguishing this tool from sibling detection tools like detect_race_conditions and detect_security_issues.

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 is provided about when to use this tool versus alternatives. The description only states what it detects, leaving the agent to infer usage context without any exclusions or recommendations.

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 responsibility for behavioral disclosure. It does not state whether the tool is read-only, what its output format is, whether it requires specific platform setup, or if it has side effects. The list of race condition types gives some behavioral insight but omits critical operational details.

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 with a clear list, making it easy to scan. The leading metadata '[mode: real, v3.1.0]' adds slight noise but does not severely hamper clarity. It earns a 4 for being concise and front-loaded.

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 an explanation of return values, usage context, prerequisites, or how 'platform' influences detection. Given the tool's complexity and lack of output schema/annotations, this description is incomplete for an agent to confidently use the 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 0%, and the description does not mention either 'projectPath' or 'platform'. The parameter meanings are not explained at all, leaving the agent to guess how to populate these fields correctly.

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 'stand-alone race-condition detector' and enumerates specific detection targets (useState-after-await, AbortController, TOCTOU, etc.), which distinguishes it from sibling tools like detect_memory_leaks or detect_logic_errors. The verb+resource structure is explicit.

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. It only lists detection capabilities and does not mention prerequisites or exclusion scenarios. Sibling tools are not referenced, so the tool's niche is implied but not clarified.

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 burden of behavioral disclosure. It does list exact scan areas (e.g., AWS/Stripe/GitHub/JWT secrets, injection types) which adds valuable context about coverage. However, it omits side effects, return format, whether it modifies files, required permissions, or runtime behavior, leaving significant gaps for a tool with zero annotation support.

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 sentence that front-loads the tool's purpose and provides a compact, scannable list of detection categories. Every word contributes value, with no filler or 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 tool is moderately complex with two parameters, one enum, and no output schema. The description fails to explain return value format, how platform influences scanning, or any preconditions/postconditions. With no annotations or output schema, the description is too incomplete for an agent to confidently invoke and interpret the tool's 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?

Schema description coverage is 0%, and the description provides no information about the parameters 'platform' or 'projectPath'. The enum values for platform are unexplained, and the description does not compensate for the lack of schema-level documentation, leaving the agent without guidance on how to set these parameters or why they matter.

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 is a 'Stand-alone security scanner' and enumerates specific vulnerability categories (hardcoded secrets, SQL/XSS/SSRF/eval injection, weak crypto, CORS misconfig, cookie flags, yaml.load). This specific verb+resource+scope distinguishes it from sibling tools like detect_memory_leaks or detect_logic_errors.

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?

Usage is implied by the 'security scanner' label, implying it should be used when security issues need to be identified. However, there is no explicit when-to-use guidance or contrast with alternatives, only the word 'stand-alone' hints at independence from other pipeline steps.

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 disclose side effects and behavior. It only mentions running checks and returning findings; it fails to mention the autoFix capability present in the schema, which could imply modification of files. It also omits any mention of permissions, rate limits, or whether the operation is read-only.

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 core description is short and front-loaded with the purpose and output. However, the leading '[mode: real, v3.1.0 headline]' is noise that adds no value for an agent and slightly detracts from the otherwise concise structure.

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 7-parameter tool with no annotations and no output schema, the description is too sparse. It does not explain the parameters, the nature of the findings, or what 'prioritized' means, nor does it clarify the autoFix behavior, making it incomplete for safe and effective 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?

Schema description coverage is 0%, so the description must compensate. It does name the five check types (race conditions, security, memory, logic, performance), which clarifies the 'checks' parameter, but it gives no guidance on output, autoFix, platforms, severityThreshold, or perCheckTimeoutMs. Most parameters remain unexplained.

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 identifies a specific verb ('vibe-check' / diagnose) with a resource (the project) and enumerates the exact scope: race conditions, security, memory, logic, and performance in parallel. It distinguishes itself from the many individual detect_* siblings by framing this as a combined sweep.

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?

Usage context is implied rather than stated: 'in one parallel sweep' suggests a comprehensive check, but there is no explicit guidance on when to choose this tool over the individual detect_* tools or any exclusions. It leaves the agent to infer that this is for a full health check rather than a targeted one.

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 present, the description carries the full burden of behavioral disclosure. It only says 'Generate ... configuration' without revealing side effects (e.g., whether it writes files), required permissions, or what the output looks like. The mode indicator is unclear and adds no 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.

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

The description is a single sentence, so it is concise, but the '[mode: real]' prefix is opaque and disrupts clarity. The information is front-loaded, but the cryptic mode tag earns it a middle score rather than higher.

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 7 parameters, no output schema, and no annotations, this description is severely incomplete. It provides no information on return values, selection logic between providers, or parameter interactions. The tool is more complex than the description acknowledges.

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 0%, and the description adds no meaning to any of the 7 parameters. It doesn't mention projectPath, provider, platform, commands, or writeToFile, leaving the agent without any semantic guidance beyond raw property names.

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: generating CI configuration for GitHub Actions, Jenkins, or GitLab CI. This is a specific verb+resource combination that distinguishes it from the sibling tools, which are all analysis/testing/fix 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, choice of provider, or scenarios where a different tool would be more appropriate. The '[mode: real]' prefix is cryptic and offers no actionable usage context.

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 full burden. It includes '[mode: real]' hinting at actual execution, but does not disclose side effects like writing to outputPath, overwriting existing files, or requiring projectPath to be accessible. Behavioral details are minimal.

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 single-sentence description is very concise and front-loaded with the main purpose, but it is under-specified for a tool with four parameters. It includes useful format details but lacks structure or additional guidance.

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 annotations or output schema and only a one-sentence description, the tool is far from complete. The description does not cover when to use the tool, what each parameter means, what behavior to expect, or what the generated report contains. It is insufficient for safe and correct 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?

Schema description coverage is 0%, so the description must compensate. It only hints at the format parameter by listing output formats, but leaves projectPath, sections, and outputPath completely unexplained. This is inadequate for a 4-parameter tool.

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 uses a specific verb ('Generate') and resource ('test automation report'), and lists the output formats (Markdown / HTML / JSON). This clearly distinguishes it from sibling tools like generate_scenarios and create_test_plan, which serve different purposes.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, expected inputs, or situations where another tool (e.g., create_test_plan) 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'generate', which suggests producing output, but fails to disclose whether files are created, what the return value is, whether analysis data is consumed, or any side effects and prerequisites beyond the implied prior analysis.

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 with no unnecessary elaboration, and it leads with the core purpose. The '[mode: real]' prefix is arguably superfluous, but the overall text is concise and front-loaded.

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 tool with five parameters, no output schema, and no annotations, one sentence is drastically insufficient. The description omits prerequisites, return values, side effects, and parameter usage, making it incomplete for reliable tool selection and 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?

Schema description coverage is 0% and the description does not explain any of the five parameters (projectPath, coverage, testTypes, focusAreas, maxScenarios). The description adds no meaning beyond the raw schema definitions, so the agent cannot infer 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 tool generates test scenarios from an analyzed app structure, with a specific verb and output resource. It does not explicitly differentiate from sibling tools like create_test_plan, but the source dependency on analyze_app_structure adds some distinction.

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 phrase 'from analyzed app structure' implies the tool should be used after analyze_app_structure, but it provides no explicit when-to-use guidance or exclusions. No alternative tools are mentioned, leaving usage decisions mostly to inference.

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?

There are no annotations, so the description must fully convey behavioral traits. It only says 'List fixes awaiting confirmation' and includes a cryptic '[mode: real]' prefix. It does not describe the output format, ordering, whether it is read-only (though implied by 'List'), or what happens when no fixes are pending. The lack of output schema further increases the burden.

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, front-loaded with the verb 'List', and contains zero filler or redundant information. It is perfectly concise for the simple function it describes.

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 that there is no output schema and no annotations, the description is too sparse. It fails to explain what a 'pending fix' is, what the return value looks like, how it connects to sibling tools like confirm_fix or apply_fix, or the meaning of '[mode: real]'. This incomplete context could leave an agent uncertain about the tool's behavior and 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?

The schema describes one parameter, projectPath, with no description (0% coverage). The description only mentions 'for the project', which loosely hints at the parameter's purpose, but it does not explicitly explain that projectPath identifies the project to query. The description adds negligible semantic value beyond the parameter's name.

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 'List fixes awaiting confirmation for the project' clearly states the specific verb ('List'), the resource ('fixes awaiting confirmation'), and the scope ('for the project'). This unambiguously distinguishes it from sibling tools like confirm_fix and apply_fix, which perform mutations rather than read-only listing.

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 usage is implied: this tool is used to retrieve pending fixes before confirmation or application. However, the description provides no explicit guidance on when to use it versus alternatives, nor does it mention any prerequisites or exclusions. The implied context is clear enough for a simple list tool, but not explicitly stated.

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 behavioral traits. It does not state whether the operation is read-only, whether it requires prior test runs, or what the response format is. The '[mode: real]' prefix adds a hint but is not explained, leaving key behavioral aspects unaddressed.

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, brief phrase with no filler words. It is front-loaded with the mode tag and main topic, but its extreme brevity borders on under-specification, keeping it from a top score.

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 with only two parameters, but the description does not cover output format, how 'recent' is defined, or how the tool relates to sibling tools. Given no output schema, the description carries more responsibility than it fulfills.

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 0%, and the description does not mention either 'projectPath' or 'limit'. The agent is left to infer parameter meaning solely from names, which is insufficient given the complete lack of explanatory text.

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 'Recent test executions for the project.' indicates that the tool returns test execution history, but it lacks an explicit verb like 'retrieve' or 'list'. It still distinguishes from siblings such as run_test or get_pending_fixes, but the noun-phrase form is less clear than a full sentence.

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. It does not mention scenarios like checking past test results or selecting it over run_scenario_test, nor does it state any prerequisites 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 carries the full burden of behavioral disclosure. It only states that content is restored from backup, but does not disclose side effects such as overwriting current file content, whether the operation is destructive, prerequisites like the fix having been applied, or error behavior if the fixId is invalid. The '[mode: real]' prefix hints at actual effects but lacks detail.

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 is front-loaded with the key action and object. It contains no filler or redundant information, making it highly concise and effective.

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 annotations, no output schema, and a minimal description, the tool lacks important context. For a rollback operation, crucial details are missing: what happens to the current files, whether the operation is reversible, whether permissions are needed, and what the backup source is. The simplicity of the parameter schema does not compensate for the lack of behavioral 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?

The input schema lists a single required parameter 'fixId' with type string, but the description provides zero explanation of what this parameter represents. The name 'fixId' is self-explanatory as an identifier, but with 0% schema description coverage, the description should explicitly state that fixId refers to the fix to roll back. It does not, leaving the agent to infer the meaning.

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: 'Restore the pre-apply file content from the backup.' It uses a specific verb ('Restore'), identifies the resource (pre-apply file content), and the source (backup), making it unambiguous. The name 'rollback_fix' further reinforces its role, distinguishing it from sibling tools like apply_fix.

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 the tool is used to undo an applied fix by restoring backed-up content, which is clear context for when to use it. However, it does not explicitly mention alternative tools or state when NOT to use it, so it falls short of explicit guideline-level clarity but remains clearly contextual.

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 mentions 'hybrid' mode and a pipeline, but doesn't explain what 'execute' does (mutations?), whether it modifies files, requires auth, or has side effects. The agent cannot infer the operational impact from this description.

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 extremely short, but not appropriately sized for a complex tool. While concise, it sacrifices essential information. The structure is a single sentence with arrows, but it's not informative enough to be effective.

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 6 parameters, no output schema, and 22 siblings, yet the description is a one-liner. It doesn't mention return values, errors, or how it integrates with the rest of the workflow. Completely inadequate for the complexity.

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 has 6 parameters with zero description coverage, and the tool description provides no parameter information. The agent must rely on names alone (e.g., 'autoFix', 'confirmMode') with no explanation of semantics, defaults, or relationships. This is a critical gap.

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 lists a sequence of actions ('Analyze → plan → execute → detect → suggest') but fails to specify the actual output or resource. It's unclear what 'full automation' entails and how it differs from sibling tools like run_scenario_test or suggest_fixes. This is more of a process description than a purpose statement.

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. It doesn't mention any prerequisites, exclusions, or scenarios where a more targeted tool would be preferable. The description only implies a one-call pipeline without contextual cues.

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 key behavioral traits such as 'regression detection' and 'auto-rollback,' which are important safety mechanisms, and 'resumeToken support' indicating resumability. However, it does not describe the tool's side effects on the project (e.g., modifying files), error behavior, or prerequisites, leaving gaps for a mutation-oriented 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 very concise, with two sentences that are front-loaded with key capabilities. It uses no filler words and effectively communicates the core purpose and notable features in a compact format.

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 (9 parameters, no output schema, no annotations), the description is far too sparse. It provides no information about return values, success/failure indicators, iteration behavior, or how the loop interacts with other tools like apply_fix or rollback_fix. The pointer to documentation is helpful but insufficient for an AI agent acting on the description alone.

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 0%, so the description must compensate for parameter meanings. It mentions 'resumeToken' and 'hybrid' (matching strategy enum), which clarifies two parameters, but the other seven parameters (projectPath, autoApply, maxIterations, acceptableThreshold, etc.) are not explained. This is insufficient for a tool with 9 parameters.

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 explicitly states the tool's function: 'Self-healing test → fix → re-test loop with regression detection, auto-rollback, and resumeToken support.' It uses a specific verb ('run') and resource ('iterative fix loop'), and clearly distinguishes from sibling tools by describing the integrated loop rather than a single action like rollback_fix or apply_fix.

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 labels the tool as 'headline tool,' which implies it is the primary entry point for iterative fixing, and references external docs for more detail. However, it does not explicitly state when to use it instead of alternatives or provide any exclusions, so usage guidance is only implied, not explicit.

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. It discloses the key behavioral trait of falling back to simulation, which is valuable. However, it does not mention side effects (real subprocess may mutate environment), permissions, failure behavior, or return format, leaving gaps in 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, efficient sentence front-loading the hybrid mode. It earns its place without fluff, though it could be slightly expanded to cover key parameters without losing conciseness.

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 6 parameters, no output schema, and no annotations, making it moderately complex. The description only covers the execution mode and omits crucial context like return values, failure handling, prerequisite setup, and meaning of 'stored scenario', leaving agents underinformed.

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 0%, and the description provides no parameter details whatsoever. It does not explain projectPath, scenarioId, platform, device, retries, or timeout, so the agent must infer meaning purely from parameter names and types, which is insufficient.

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 states the specific verb 'Run' with a clear resource ('a stored scenario') and adds the distinctive hybrid mode ('real subprocess where possible, falls back to simulated'), which differentiates it from siblings like run_simulation and run_stress_test.

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 clear usage context: this is for running stored scenarios with real execution when possible and simulation as a fallback. It does not explicitly name alternatives or when-not-to-use conditions, but the mode hint provides enough context for an agent to decide.

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 burden. It discloses the simulated mode and the random/sequential nature of the simulation, which is useful behavioral context. However, it does not explain potential side effects, output format, or resource impact, leaving gaps in 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 concise sentence that efficiently captures the core purpose and mode. It is front-loaded with the mode indicator, but it is so brief that it sacrifices substantive detail, still earning its place without waste.

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 5 parameters, no annotations, no output schema, and numerous siblings, the description is too sparse. It does not explain return values, how to interpret results, or when to use this tool over other similar simulation tools, leaving significant contextual gaps.

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 0%, so the description should compensate. It only hints at 'random / sequential' which maps to the userPatterns enum, but provides no explanation for duration, intensity, or monitorMetrics. The description adds minimal parameter meaning beyond the raw schema.

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 states the tool performs user-behavior simulation with random or sequential patterns to find issues. It clearly identifies the action and resource, and the purpose differentiates it somewhat from sibling tools like run_stress_test or run_scenario_test, though it does not explicitly name alternatives.

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 is given on when to use this tool versus siblings such as run_scenario_test or run_stress_test. The description implies usage for general user-behavior simulation, but there is no mention of preferred contexts, exclusions, or alternative 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?

No annotations are present, so the description carries full responsibility. It mentions '[mode: hybrid]' which hints at behavior but does not explain what hybrid means or disclose side effects like resource consumption, potential system impact, or whether it modifies state. This is a significant gap for a load test 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 a single concise sentence, but it is under-specified for a tool with 6 parameters. It omits critical details and reads more like a label than a useful description. The brevity detracts from its utility rather than enhancing it.

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 6 parameters, no annotations, and no output schema, the description is severely incomplete. It does not explain return values, expected behavior, prerequisites, or possible side effects. The complexity of the tool demands far more context than this single sentence provides.

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 0%, and the description adds no parameter-level meaning. It refers indirectly to 'endpoint or UI surface' which loosely maps to targetType and endpoints, but does not explain projectPath, concurrency, duration, or rampUp. The description fails to compensate for the absence of schema descriptions.

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 a concurrency/load test against an endpoint or UI surface, using a specific verb ('load test') and resource. It does not explicitly differentiate from sibling tools like run_scenario_test or run_simulation, but the focus on load/concurrency is a distinct 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. Sibling tools such as run_scenario_test and run_simulation overlap conceptually, but the description does not mention when to prefer run_stress_test, nor does it state prerequisites 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 bears full responsibility for disclosing behavioral traits. While 'suggestions' implies non-destructive behavior, the description does not explicitly state that no files are modified, nor does it mention any permissions, side effects, or mode semantics ('real'). This is insufficient transparency for a tool that could be confused with fix application tools.

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 with no redundant phrasing. The '[mode: real]' prefix is unexplained and adds a slight ambiguity, but overall the text is appropriately concise and scannable.

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 tool with three parameters and no output schema, the description is too sparse. It does not explain return values, the effect of maxSuggestions, what happens if issueIds is omitted, or the meaning of 'mode: real'. This incomplete context would require the agent to guess or inspect the schema deeper.

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 description mentions none of the schema parameters (projectPath, issueIds, maxSuggestions). Since schema description coverage is 0%, the description entirely fails to add meaning to the parameter structure. An agent would have no idea what values to pass or how they affect behavior.

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: 'Generate rule-based fix suggestions for detected issues.' It uses a specific verb ('generate') and resource ('fix suggestions'), and the phrase 'rule-based' adds specificity. This distinguishes it from sibling tools like apply_fix (applies fixes) and rollback_fix (rolls back changes).

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. It does not mention prerequisites (e.g., having detected issues first), exclusions (e.g., not for applying fixes), or typical workflows. This absence leaves the agent to infer usage from the name alone.

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