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 explains the detection methodology and gives a basic idea of the return value ('flagged actions with pending requests'), but it does not disclose side effects, input constraints beyond the schema, potential failure modes, or performance considerations. For a read-only analysis tool, more behavioral context is expected.

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 concise and well-structured: two sentences front-load the core purpose and then provide the key output detail. No redundant information or filler.

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 partially explains the return value ('flagged actions with pending requests') but lacks detail about the structure or format of that output. Given the tool has only one parameter and is specialized, the description is minimally complete but leaves gaps about the exact nature of flagged actions.

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 the sole parameter 'trace_path' completely (100% coverage), including both file and URL options. The description adds no additional parameter semantics, so the baseline of 3 applies per the rubric.

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 with a specific verb ('Detects') and resource ('potential race conditions'), and explains the mechanism (network requests in-flight when user interaction fired). This distinguishes it from sibling tools like get_filtered_network_logs or correlate_dom_and_network.

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 investigating race conditions but does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It provides contextual hints but no explicit guidance on tool 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 are provided, so the description carries the full burden. It discloses the analysis steps (aligns by sequence, finds first divergence, summarises network differences) and implies a read-only comparison. However, it does not describe output format, needed permissions, or edge cases (e.g., traces not from same test). This is moderate 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 compact sentences that front-load the main action and follow with specifics. Every sentence adds value, with no filler.

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 covers purpose, method, and use case. It lacks output details, but the tool has no output schema and siblings likely share similar output patterns. Given only two well-documented parameters, the description is sufficiently complete for an AI agent to select and invoke it correctly.

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 descriptions are minimal but cover 100% of parameters. The tool description adds the semantic relationship that the two paths are passing and failing traces of the same test, which is helpful but not extensive. Baseline 3 applies.

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 a specific verb ('Compares') with the resource ('a passing and a failing trace of the same test') and details the comparison approach (aligns actions, finds divergence, summarises network differences). This distinguishes it from sibling tools like get_trace_summary or get_action_timeline which operate on a single trace.

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?

Explicitly states its use case: 'Use to diagnose flakiness — what was different in the run that failed.' It communicates when to apply the tool, though it does not mention exclusions or alternative sibling tools. Context is clear.

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 discloses important behaviors: filtering out 'background polling and analytics pixels,' using a ±100ms timing window, and returning body snippets. It does not mention error handling or edge cases, but the key behavioral traits are covered.

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?

Three sentences, each earning its place: first states the core action and return value, second describes filtering, third gives the use case and benefit. Front-loaded with the main purpose, no redundancy, appropriately sized for the complexity.

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 having no output schema, the description explicitly lists the return fields (request URL, status, body snippet, DOM nodes). It also provides timing details, filtering criteria, and a concrete use case. With a single required parameter and no additional configuration, this is fully complete.

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 covers the only parameter (trace_path) at 100% coverage, so the baseline is 3. The description does not add any additional meaning about how to use the parameter or its format beyond what the schema already states. This is acceptable because there is nothing missing.

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 ('Joins') with a clear resource ('HAR network log and DOM snapshots') and states the output (triggering request URL, response status, body snippet, DOM nodes). It explicitly distinguishes this from sibling tools by focusing on building an 'explicit causal chain' rather than just listing events.

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 clear when-to-use guidance: 'Use to diagnose race conditions and async rendering bugs.' It also explains the value in removing hallucination from causal questions. It does not explicitly name alternatives or exclusion cases, but the usage context is strong.

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 behavioral burden and does so thoroughly. It discloses the specific thresholds (gaps >50ms, actions >500ms), the types of analysis (frame timestamps, concurrent network requests, monotonic duration checks), and the output format (ranked list with suspected_cause). This gives an agent a complete understanding of what the tool will do.

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 tightly structured into three sentences: what it detects, how it analyzes, and what it returns with a clear purpose. Every clause adds information without redundancy. It is dense but not verbose, earning a high score for conciseness and logical organization.

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 and lack of output schema, the description sufficiently covers input (trace_path implied), processing methods, and return type (ranked anomalies with suspected_cause). It omits edge cases like invalid trace paths or behavior when no anomalies are found, but these are not critical for an agent to use the tool effectively. The description is complete enough for a tool of 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 100%, so the baseline is 3. The description adds semantic value by explaining how the frame_drop_threshold_ms and slow_action_threshold_ms parameters relate to the analysis logic (e.g., 'gaps > 50ms' and 'actions longer than 500ms'). It does not explicitly name the parameters but provides behavioral context beyond the 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's purpose: detecting Long Tasks and frame drops that cause Playwright timeouts. It specifies the exact analysis performed (screencast-frame timestamps, main-thread blocking, slow actions, concurrent network requests, monotonic action durations) and distinguishes itself from sibling tools like analyze_race_conditions by focusing on performance-based timeout causes.

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 implicit usage guidance by stating the tool helps distinguish blocked main thread, network saturation, or navigation timeout 'without blaming a missing element.' This suggests using it when a timeout may be performance-related rather than due to a missing element, but it does not explicitly name alternatives or provide direct when-to-use/when-not-to-use 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 carries the full burden. It discloses key behaviors: output is base64 JPEG screenshots, selection is based on a temporal window, and results are associated with test step titles. This goes beyond a minimal statement, though it doesn't cover edge cases or error handling.

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, information-dense sentence that efficiently conveys the action, output format, temporal scope, and result association. No redundant words; every phrase adds distinct value.

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 provides a basic idea of the return value (screenshots with step titles) but lacks explicit structural details such as whether it returns a list, ordering, or how 'resolved' is presented. Given moderate complexity, the description is adequate but leaves some ambiguity.

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% with clear descriptions for all four parameters (trace_path, limit, lookback_ms, lookforward_ms). The description reiterates the temporal-window concept but adds no extra semantics beyond what the schema already provides, so the 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's function: extracting key screencast screenshots (base64 JPEG) from a temporal window around failure or trace end, with associated test step titles. It explicitly distinguishes itself from the sibling get_screenshot_at_failure by emphasizing multiple screenshots within a window and the step-title resolution.

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 clear context for when to use the tool: to capture visual evidence from a window around a test failure or the end of the trace. It doesn't explicitly mention alternatives or when-not-to-use, but the temporal-window context implies its specific use case among siblings.

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, and it delivers: it reveals handling of .pwtrace.zip extensions, multi-retry archives (identifies which retry failed), and all three HAR modes. It also states it confirms trace validity. It does not detail error behavior on invalid traces, but for a read-only inspection tool, the disclosure is substantive.

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 sentences, front-loaded with the core action and outputs, then expands with relevant edge-case details and usage guidance. Every sentence contributes value without redundancy or fluff.

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 covers what the tool returns (format version, retry breakdown, HAR mode), when to use it, and key compatibility aspects. It lacks an explicit statement of return format/error behavior, but given the single-parameter schema and read-only nature, it is reasonably complete.

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 already provides full coverage for the single parameter trace_path with a description ('Absolute path to trace.zip, or a URL (https://) to download it from'). The tool description does not add additional parameter-specific semantics beyond mentioning .pwtrace.zip compatibility, which is more about tool behavior than the parameter's meaning. Baseline 3 applies.

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: 'Strictly inspects a Playwright trace archive and returns format version, retry session breakdown, and HAR payload mode.' It uses a specific verb ('inspects'), identifies the resource ('Playwright trace archive'), and enumerates specific outputs. It also distinguishes itself from sibling tools by positioning itself as a preflight validation tool ('Use before other trace 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?

Provides explicit usage context: 'Use before other trace tools when the archive may be from an unfamiliar Playwright version or CI configuration — confirms the trace is valid and tells you what data is available.' This tells the agent when to use the tool but does not explicitly state when not to use it or name alternatives, so it falls slightly short of a 5.

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 discloses normalization behavior (stripping paths, numbers, UUIDs) and hash stability, giving insight into how the tool works. It does not mention potential error behavior or side effects, but for a read-only analysis tool this is adequate.

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, front-loaded with purpose, no unnecessary words. Every sentence earns its place, making it highly concise and well-structured.

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 single-parameter tool with no output schema, the description adequately explains the tool's function, normalization process, and intended use case. It is complete for the tool's 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 coverage is 100% with a clear description of trace_path (absolute path or URL). The tool description adds no additional parameter detail beyond the schema, so the baseline 3 applies.

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 generates a stable 12-char hash signature for test failures by normalizing error messages. This specific verb+resource+scope distinguishes it from sibling tools that analyze other aspects of traces.

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?

Explicitly states use case: grouping duplicate failures across parallel CI runs without manual reading. It implies an alternative (reading traces manually) but does not explicitly name specific sibling tools 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 reveals that the result is paginated and includes locators and timings, which is useful. However, it does not clarify ordering, defaults, or potential side effects such as downloading a trace from a URL, leaving some behavioral ambiguity.

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, with the primary purpose front-loaded and no redundant phrasing. The second sentence provides practical pagination guidance, ensuring every word earns 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?

For a straightforward list tool with fully documented parameters and no output schema, the description adequately covers the tool's purpose and pagination usage. It omits details like default ordering and the necessity of trace_path, but the schema covers that requirement, so the description is mostly complete for the tool's 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 100% for all three parameters, which establishes a baseline of 3. The description adds value by explicitly explaining the purpose of limit/offset for pagination ('Use limit/offset to page through large traces'), which goes beyond their individual 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 states 'Returns a paginated timeline of all actions with locators and timings,' which is a specific verb and resource with clear scope. This distinguishes it from sibling tools like get_trace_summary or get_causal_chain_for_failure, which target different aspects.

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 clear context for when to use the tool: when you need the full action timeline with locators and timings. The phrase 'Use limit/offset to page through large traces' directly guides handling large results. However, it does not explicitly mention alternatives or exclusions, so it stops short of a 5.

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 disclosure. It states the return format (YAML), the default selection of the failed action or last snapshot, and the token cost reduction. This is meaningful behavioral context, though it does not describe edge cases like missing traces or performance implications.

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 well-structured sentences with no filler. The first sentence front-loads the core action and resource, the second explains cost and parameter behavior. Every clause earns 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 is simple (2 params, 1 required) and the description covers the essential behavior: return type, default action selection, and cost rationale. The absence of an output schema is mitigated by specifying 'YAML' as the format, and no prerequisites or error conditions are necessary for this straightforward getter.

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 reinforces the action_index default behavior but does not add new meaning beyond the schema's own parameter descriptions. It adds no extra detail for trace_path, so it does not exceed the 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 specifies the action ('Returns') and the resource ('ARIA accessibility tree (YAML) for a frame snapshot in the trace'). It also differentiates from sibling tools by highlighting the 90% token cost reduction vs raw HTML, making its purpose distinct.

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 clear context for when to use the tool (e.g., to access an ARIA tree for a specific action or failure analysis) and explains the default behavior of action_index. While it does not explicitly mention alternative tools, the cost advantage and frame-snapshot scoping give implicit guidance without being misleading.

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 carry the full burden of behavioral disclosure. It does add useful context: the tool traverses backwards in time, combines action/network/console error data, and outputs a root-cause finding. However, it does not explain output format, limitations, or any side effects, so it 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?

The description is two sentences, front-loaded with the main action, and every clause adds information. There is no redundancy or filler.

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 no output schema, the description gives a high-level result (surfaces the most likely root cause) but does not specify the structure of the returned chain or how to interpret confidence. Given the moderate complexity and full parameter schema, the description is largely sufficient but could be enhanced with 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?

The input schema covers 100% of parameters (trace_path and lookback_ms) with descriptions, so the baseline is 3. The tool description adds no additional parameter-level meaning, so it neither enhances nor detracts from 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 uses a specific verb ('walks backwards') and identifies the resource ('failed action') and the output ('chronological chain of preceding actions, network errors, and console errors'). It clearly distinguishes from sibling tools like get_action_timeline or get_console_errors by emphasizing causal-chain building and root-cause surfacing.

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 root-cause analysis after a failed action but does not explicitly state when to prefer this tool over alternatives such as analyze_race_conditions or get_trace_summary. No exclusions or alternative guidance are provided, so the usage context 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?

No annotations are provided, so the description carries the full burden. It states the output type and pagination but lacks details such as whether it performs a network download for URL trace paths, ordering of results, return format, or side-effect-free read behavior. 'Returns' implies read-only but is 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 two sentences, front-loaded with the primary action, and contains no redundant or filler content. Every sentence earns 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?

For a simple list-retrieval tool, the description covers the core function and pagination. However, with no output schema and no annotations, it omits return structure and edge-case behavior, leaving it adequate but not fully complete.

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 description's pagination phrase adds minimal context for limit/offset but does not add new semantics beyond what the schema already documents.

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 'Returns' with the resource 'JS exceptions and warnings', clearly identifying what the tool does. It distinguishes itself from sibling trace-analysis tools like get_filtered_network_logs or get_trace_summary by focusing exclusively on console errors/warnings.

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. The only usage hint is 'Use limit/offset to page through results', which addresses pagination but does not clarify when this tool should be preferred over sibling 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 exist, so the description carries the burden of behavioral disclosure. It explains that the tool returns added and removed elements and highlights the efficiency benefit. However, it does not mention error handling, prerequisites (e.g., valid trace/action index), or limitations (e.g., ARIA tree availability), leaving meaningful gaps.

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 concise sentences with no redundant wording. The first sentence leads with the core action, and the second explains the value proposition. Every phrase adds 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?

The description adequately covers purpose and return value for a diff operation. It does not include an output schema, but explains what is returned. The parameter description already references get_action_timeline for obtaining action_index, so the tool description remains sufficiently complete for an agent to invoke it correctly.

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 both parameters (trace_path, action_index) have detailed descriptions. The tool description adds no extra semantic information about parameters, so the baseline score of 3 applies.

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 uses a specific verb 'Diffs' and identifies the resource 'ARIA tree' with a clear scope 'before and after a specific action'. This distinguishes it from sibling tools like get_aria_accessibility_tree (full tree snapshot) and compare_traces (whole trace comparison).

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 gives clear context for when to use the tool: when the agent needs to see exactly what changed after an action without comparing full DOM dumps. It implies the use case but does not explicitly name alternatives or state when not to use it, so it falls short of a 5.

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 behavioral traits. It does not mention whether the operation is read-only, how it handles missing data, or what format the attributes are returned in. The description is a bare functional statement without edge cases or limitations.

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 directly states the tool's purpose with no unnecessary words or repetition. It is well-structured 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 tool is simple with one parameter and no output schema, so the description covers the basic function. However, it lacks context on return format, failure behavior, or typical use cases, leaving gaps that an agent might need to resolve. It is adequate but not exhaustive.

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 (trace_path), so the schema already explains the parameter adequately. The description adds no additional semantic value beyond what the schema provides.

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 a specific verb ('Returns') and resource ('DOM attributes of the failing element') along with a time scope ('at the moment of failure'). This effectively distinguishes it from sibling tools like get_screenshot_at_failure (visual) or get_causal_chain_for_failure (causal analysis).

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. The description only states what it does without any context on appropriate scenarios, prerequisites, or situations where other sibling tools would be preferable.

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 adds value by explaining the filtering logic (4xx/5xx only, stripping static assets) and pagination, which are not evident from the schema. However, it does not detail error handling or response format.

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, front-loaded with the core purpose, and contains no unnecessary words. Every sentence serves a purpose: the first describes what is returned, the second explains pagination.

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, so the description should compensate by explaining return values. It describes the filtering and pagination but does not clarify the format of the returned network responses (e.g., JSON structure, headers, status codes). It is adequate for a simple list tool but leaves a moderate 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 for parameters is 100%, so the baseline is 3. The description mentions limit and offset but does not add meaning beyond what the schema already provides. It does not introduce any additional parameter context.

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 returns only 4xx/5xx network responses while stripping static assets, using a specific verb and resource. This distinguishes it from sibling tools by specifying the exact filtering behavior.

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 retrieving filtered network logs and mentions pagination, but it does not explicitly state when to use this tool versus alternatives or when not to use it. It provides context but no clear exclusions or alternative 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 the full burden. It discloses the output format (base64 JPEG) and the selection behavior (nearest to failure or specific index). However, it does not mention edge cases like missing screenshots or invalid indices, which prevents a perfect score.

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 only two sentences long, front-loaded with the core purpose, and every clause earns its place—purpose, usage trigger, and parameter behavior are all covered without 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?

For a simple retrieval tool with no output schema, the description sufficiently explains the return format (base64 JPEG) and the two modes of use. It could add error-handling notes (e.g., what happens if no screenshot exists), but overall it provides enough context for an agent to invoke the tool correctly.

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 both parameters are already described in the input schema, including the 0-based indexing and the omit-to-get-failure behavior. The description essentially repeats this information without adding meaningful new semantics, so it stays at 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 verb 'Returns' and the resource 'screenshot (base64 JPEG) from the trace', with specific scope 'closest to the moment of failure'. It also distinguishes from siblings by explicitly referencing get_aria_accessibility_tree as an alternative.

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 gives an explicit when-to-use condition: 'Use when get_aria_accessibility_tree returns an empty or unhelpful tree'. It also explains the choice between default (nearest to failure) and passing screenshot_index, providing clear decision guidance.

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 clearly states what is returned, and the verb 'Returns' implies a read-only operation. However, it does not disclose any potential behavioral details such as network download when the trace_path is a URL, time costs, or error handling. This is a minor gap given the simplicity of the 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, focused sentence with no redundant information. It lists the return fields efficiently and is easy to parse at a glance.

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 metadata retrieval tool with one well-documented parameter and no output schema, the description is complete. It explains exactly what the tool returns, and the schema covers the input. No additional context is necessary.

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 single parameter trace_path has 100% schema description coverage, explaining it accepts a path or URL. The tool description adds context about what fields will be returned but does not add any additional parameter-specific semantics beyond the schema. 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 verb 'Returns' plus the resource 'test metadata' clearly states what the tool does. Enumerating the exact fields (title, browser, platform, viewport, start time) distinguishes it from sibling tools like get_trace_summary or get_element_state_at_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?

The description implies the tool is used when you need basic test metadata (title, browser, etc.), but it does not explicitly state when to use it over alternatives or mention exclusions. There is no guidance about when not to use this tool, unlike the get_calls example which names a sibling alternative.

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?

Annotations are absent, so the description carries the full responsibility for behavioral disclosure. It indicates a read operation via 'Returns' but does not explain behavior on invalid traces, output structure, or error handling—significant gaps for an unannotated 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, efficient sentence that directly states the tool's function without any wasted words. It is well-structured and immediately 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?

The tool is simple (one parameter), but without an output schema, the description should clarify the return structure. It mentions two data items but not how they are packaged, which creates ambiguity for an agent needing to parse the result.

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 provides a detailed description of trace_path (path or URL), giving 100% schema coverage. The tool description adds no additional semantic meaning, so the baseline score of 3 applies.

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 specifies a clear action ('Returns') and resource ('failing action and top-level error message') from a Playwright trace. However, it does not explicitly distinguish itself from sibling tools like get_causal_chain_for_failure or generate_error_signature, which might also summarize trace failures.

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. There is no mention of use cases, prerequisites, or exclusions, leaving the agent to infer applicability from the description 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?

The description discloses that it uses the execution stack from the test runner, which is useful internal context. However, with no annotations provided, it does not detail potential limitations, error behavior when no mapping is found, or whether the operation is purely read-only. A more thorough disclosure would be beneficial, but the basic behavior is stated.

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 specifies the method (execution stack). It contains no fluff and earns its place with essential 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 only 2 parameters and no output schema, the description covers the main functionality but could be more explicit about the return format (e.g., file path and line number) and any prerequisites (e.g., requiring a test runner trace). It is close to complete for a simple mapping tool, but a few details are missing.

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 already provides full descriptions for both parameters (trace_path and action_index) with 100% coverage. The description mentions 'action index' but does not add significant extra meaning beyond the schema, such as how to derive the index from the trace or edge cases. 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's function: mapping a failing browser interaction or action index to a specific line of code in the test file using the execution stack. This is a specific verb+resource that distinguishes it from siblings like get_trace_summary or get_causal_chain_for_failure, which focus on other aspects of the trace.

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 locate the source line for a failing action, with an optional action_index parameter to target a specific step. However, it does not explicitly mention alternatives or when not to use this tool, so it gives clear context but no 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?

The description transparently says it deletes screenshots and returns sizes/path, but it does not explicitly state whether the original trace file is preserved. The divergence_only parameter's false behavior (leaving screenshots intact) is explained but raises ambiguity about what the tool actually produces in that mode. Without annotations, this leaves moderate 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?

The description is two sentences, front-loaded with the verb 'Shrinks', and contains no fluff. It efficiently communicates the deletion criterion and the return value, making every sentence earn 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?

For a tool that modifies a trace archive, the description is fairly complete but leaves notable gaps: it doesn't explicitly state that the original file is preserved, doesn't explain behavior when no failure exists, and doesn't resolve the divergence_only=false output behavior. With no annotations or output schema, these gaps are more significant than they would be otherwise.

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 provides thorough descriptions for both parameters (100% coverage). The tool description adds the specific failure window (t_fail - 5s to t_fail + 1s) and mentions the output format, providing some context. However, it does not fully clarify the behavior when divergence_only is false, leaving ambiguity about what is returned in that case.

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 (Shrinks) with a clear resource (Playwright trace zip file) and specifies the exact deletion criterion (screenshots outside the critical failure window t_fail - 5s to t_fail + 1s). It also states the output (original vs trimmed size and path to the new archive). This differentiates it from the sibling tools, which are all read-only analysis 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 implies the tool is used to reduce trace size by keeping only the critical failure window, but it does not explicitly contrast with sibling tools or state when not to use it (e.g., if the complete trace is needed). While no sibling tool performs trimming, the lack of explicit alternative guidance leaves the usage context only implied.

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