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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the tool closes browsers and resets session state, which implies destructive/state-changing behavior. However, it doesn't disclose potential side effects like losing unsaved data or whether it affects other sessions. The description is adequate but could be more transparent about consequences.

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 short sentences with no wasted words. The primary action is stated first, followed by usage guidance. 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 zero-parameter tool with no output schema, the description is mostly complete. It states what it does and when to call it. However, it doesn't mention what happens after cleanup (e.g., can the session be resumed?) or whether this is reversible. Given the tool's simplicity, this is a minor 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 tool has zero parameters, so there are no parameter semantics to document. The description correctly implies no inputs are needed. Baseline 4 is appropriate for a zero-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 clearly states the tool's function: closing all open browsers and resetting session state. It uses a specific verb ('close', 'reset') and identifies the resource (browsers, session state). It doesn't explicitly differentiate from siblings, but the action is distinct enough among the listed tools.

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

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

The description provides clear context for when to use the tool: 'Call when done testing.' This implies it should be used at the end of a testing session, which is useful guidance. It doesn't explicitly mention alternatives or when not to use it, but the 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?

With no annotations, the description carries the full burden. It discloses idempotency and preservation of existing VIBE.md and vibe.config.json files, which are useful behavioral notes. However, it does not mention side effects, error conditions, authentication requirements, or what happens when files are absent. The disclosure is partial but not contradictory.

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 and adds two key behavioral guarantees (idempotency and file preservation). There is zero waste; every clause earns its place. Structure is optimal for a tool with such a narrow scope.

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 lacks an output schema, so the description should explain what the tool returns or how the agent should interpret the result. It does not. It also omits details about failure modes, the impact of the optional url parameter, and any prerequisites like existing project structure. For a tool that performs detection and configuration, this is incomplete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds no extra meaning to the parameters beyond what the schema already states. It neither clarifies the url parameter's role nor the codebase_path format. The schema is adequate, so a 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 purpose: configuring a project and detecting its framework, running server, and authentication method. It names the resource (project) and the actions (configure, detect). While it does not explicitly distinguish from siblings, the sibling list contains no overlapping tools, so clarity is high.

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 or when not to use it. There is no mention of exclusions, prerequisites, or conditions that would lead an agent to choose this tool over another. The purpose is implied but not explicitly framed against other options.

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 behavioral disclosure burden. It does well by detailing what executing a scenario produces: step-by-step logs, screenshots after each state-changing step, API errors observed, and final page state. It does not mention potential side effects on the application under test, but the disclosed execution details are 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?

Two tight sentences: the first front-loads the core action and outputs; the second adds provenance guidance. No filler, repetition, or unnecessary detail.

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

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

The schema richly covers the nested scenario and step fields, and the description covers return behavior since there is no output schema. It is generally complete for calling the tool, though it would be stronger with explicit guidance on when to choose this versus run_full_test and a note about side effects.

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 itself adds little parameter-level semantics beyond noting that scenarios may be constructed from scan_codebase output, but the schema already documents scenario, authenticated, and all nested step fields thoroughly.

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 names the verb ('execute'), the resource ('a single test scenario'), and enumerates the step types (navigate/fill/click/assert) and result contents (logs, screenshots, API errors, final page state). The word 'single' helps distinguish it from sibling run_full_test, which suggests a broader full-suite execution.

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 useful context that scenarios can be built from scan_codebase output or created custom, which implies a valid usage pathway. However, it does not explicitly state when to prefer this tool over alternatives like run_full_test or run_converge, nor does it provide 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 of behavioral disclosure. It does disclose the interactive actions, API observation, and output format. However, it does not warn that clicking buttons and filling inputs may mutate data, trigger side effects, or navigate away from the current state—an important gap for an autonomous exploration 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 reasonably concise, front-loaded with the core action, and provides useful detail about outputs. The second sentence adds the 'senior tester' framing but partially repeats the earlier 'tries every element and reports' idea, creating slight redundancy.

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

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

The description explains return values (interaction outcomes, API observations, screenshots) because no output schema exists. However, for a tool that performs full interactive exploration and can alter state, it is incomplete without noting prerequisites (e.g., app must be running), potential side effects, or the destructive nature of the actions it performs.

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 schema already documents both route and authenticated. The description adds no parameter-level detail beyond that, such as when authenticated should be true or how the route interacts with the app's base URL. 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 states a specific verb and resource: 'Perform a full interactive exploration of a page' and enumerates concrete actions (click buttons, fill inputs, test tabs, observe API calls). It is clearly distinguished from siblings like scan_page_elements or take_screenshot by presenting itself as the 'senior tester' mode that tries every element and reports what works and breaks.

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 when to use it: when you need exhaustive interactive exploration rather than a static scan. However, it never explicitly contrasts it with siblings such as scan_page_elements or execute_scenario, nor does it state when not to use it (e.g., when a non-destructive read-only scan is safer).

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. It discloses that the report auto-opens in the browser (a side effect) and that it is self-contained, which is valuable behavioral context beyond what annotations would provide. It doesn't mention if it requires prior data or if it's safe, but it does add useful 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 well-structured paragraph that starts with the core purpose, then lists contents, then gives usage guidance. It is not overly verbose, but the list of contents could be trimmed for brevity. Still, each 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?

The tool is relatively simple with a single optional parameter and no output schema, so the description covers the essential information: what it produces, when to call it, and a key behavior (auto-open). It does not explain the return format in detail, but that's not required given no output schema. It's adequate 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% as the only parameter 'title' is described as 'Optional custom title'. The description doesn't add any additional meaning beyond this, so baseline 3 is appropriate.

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

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

The description clearly states the tool generates an HTML test report with embedded screenshots, and lists the specific contents (pass/fail results, screenshots, exploration findings, etc.), which distinguishes it from siblings like take_screenshot or get_coverage.

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 explicitly says to call this after executing scenarios and explorations, which provides clear when-to-use guidance. However, it doesn't mention when not to use it or name alternatives, so it loses a point for lack of exclusions or alternatives.

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

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

No annotations are provided, so the description must carry the full behavioral burden. It mentions that output is budget-capped, which is a limitation, but it doesn't disclose whether the operation is read-only, if it has side effects, or how errors are handled. Since it retrieves data, the read-only nature is implied but not stated. This is a significant gap for a tool with zero annotation coverage.

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

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

The description is two sentences with no filler. It front-loads the core purpose and then adds usage guidance. It is concise and structured effectively, though slightly longer than strictly necessary, it earns its length.

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?

There is no output schema, so the description should explain what the tool returns. It says 'Returns actual source code' but doesn't specify the format (e.g., list of files with content, metadata) or how budget-capping affects the output. Error behavior and edge cases (e.g., feature not found) are not covered. For a moderately simple tool, this is acceptable 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%, and both parameters are well-documented in the schema. The description adds no extra semantic detail about the parameters themselves; it only contextualizes the return value. With full schema coverage, a baseline of 3 is appropriate.

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

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

The description clearly states the action (retrieve) and the resource (source files for a given feature or route). It explicitly says it returns actual source code to understand field names, endpoints, and structure, which distinguishes it from siblings like scan_codebase or scan_page_elements by focusing on a specific feature.

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 explicitly instructs to call this after scan_codebase and when writing precise test scenarios for a specific feature. It provides a clear usage condition and rationale, though it doesn't explicitly name alternative tools for different use cases. The context is sufficient for an agent to decide when to invoke it.

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. It discloses the prerequisite, the JSON return structure, and the three main components (coverage entries, gap analysis, suggestions). It does not state what happens when the prerequisite is unmet, but for a read-only lookup this is reasonably complete.

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 and front-loads the core function. The final sentence restates the purpose somewhat redundantly, but overall the length is appropriate 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?

Given no output schema and no parameters, the description compensates well by spelling out the return shape and prerequisite. The overlap with suggest_tests is not directly addressed, leaving minor ambiguity about when to prefer this over that sibling.

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 tool has zero parameters)Skip baseline 4 applies since the description does not need to compensate for missing parameter documentation.

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?

States a specific verb ('Return'), a clear resource ('test coverage map'), and the additional outputs ('gaps' and 'suggested tests'). Despite the sibling suggest_tests, the description clearly frames this as a coverage-status tool rather than a test-generation tool.

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 names the prerequisite (scan_codebase must have been called first) and gives a direct use case ('Use this to understand what has been tested and what still needs testing'). It does not name alternatives or exclusions, but the context is clear enough for an agent to select it.

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 fully disclose behavioral side effects. It states it establishes an authenticated session and returns data, but omits critical side effects such as session persistence, cookie/token storage, or modification of saved credentials. Insufficient for a stateful 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?

Two sentences, front-loaded with purpose and return values, followed by credential behavior. No unnecessary words or repetition; all content 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 stateful login tool with no annotations and no output schema, the description is incomplete: it omits failure modes, prerequisites, side effects, and does not differentiate from sibling tools. An agent cannot fully predict the tool's impact or limitations.

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 covers all parameters at 100%, so baseline 3 applies. The description adds minor value by explaining the saved-credential fallback behavior for email and password, but otherwise repeats schema information.

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?

Clear verb 'establish' and resource 'authenticated browser session' with mention of login scenario and return values. Does not explicitly distinguish itself from sibling execute_scenario, but purpose is apparent from the login-specific scope.

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?

Offers no guidance on when to use this tool versus sibling tools like execute_scenario or explore_page. The credential fallback behavior is useful but does not help an agent choose between tools.

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

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

With no annotations provided, the description carries full disclosure burden. It discloses the iterative process (baseline then follow-up rounds), stopping conditions (pass rate and gap thresholds or max rounds), and output behavior (returns results across all rounds and opens the final HTML report). It doesn't mention potential side effects on the app or resource usage, but for a testing tool the core behavior is well 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?

Two sentences with zero waste. The core function is front-loaded, followed by the use-case and output behavior. Every clause adds value, making it highly scannable for an agent.

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?

There is no output schema, so the description must explain the return value, and it does ('Returns results across all rounds and opens the final HTML report'). It also explains the iterative process and stopping criteria, covering all essential behavioral aspects. The schema handles parameter details, so the description is 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%, so all parameters already have clear descriptions. The tool description doesn't add any parameter-specific meaning beyond what the schema provides, which meets the baseline for high coverage. The mention of 'targeting coverage gaps' indirectly relates to target_pass_rate and max_high_severity_gaps, but it's not explicit enough to exceed 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 states a specific verb ('runs iterative coverage') and resource (full baseline suite plus follow-up rounds), and clearly distinguishes it from siblings by mentioning 'targeting coverage gaps and failed scenarios' until thresholds are met. The use-case phrase 'keep testing until coverage is good' further clarifies its unique role.

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 explicitly says 'Use for "keep testing until coverage is good"', which is a clear when-to-use signal. It doesn't explicitly contrast with alternatives like run_full_test or execute_scenario, but the iterative nature and explicit use-case give sufficient guidance for an agent to select it.

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 behavioral disclosure. It outlines the high-level steps but omits potential side effects (e.g., whether it modifies the codebase, creates artifacts, or requires a running application) and does not mention duration or resource usage. It adds moderate value by describing the pipeline but lacks critical operational details for a complex, state-changing operation.

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 the main purpose and step list, followed by the alternative. Zero wasted words. The structure is optimal for quick parsing by an agent.

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 tool with 4 optional params and no output schema, the description adequately covers purpose and usage guidance. It does not explain what the generated report contains or where it is output, and it omits prerequisites like a running application. However, given the schema covers all parameters, the description is largely complete; the missing behavioral details keep it from a 5.

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 with descriptions, so the baseline is 3. The tool description adds no parameter-specific guidance beyond the schema, e.g., it doesn't explain the interaction between mode and the pipeline steps. It neither improves nor degrades the schema's clarity, so a 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 runs a complete end-to-end test suite, enumerating the pipeline steps (scan → generate scenarios → execute → explore → report). It explicitly labels itself as the 'all-in-one' command, distinguishing it from the granular sibling tools. The verb 'run' and resource 'complete end-to-end test suite' are specific and unambiguous.

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

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

It directly tells the agent when to use this tool (for full pipeline) and when not to (for granular control), naming the specific sibling tools to use instead. This is explicit exclusion guidance, leaving no ambiguity about 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, the description carries the behavioral burden. It discloses the return type (ProductModel) and the contained data, and the verb 'Analyze' implies a read-only operation. However, it does not explicitly state side effects, whether it modifies anything, or any operational constraints like time or dependency on a running app.

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 dense sentences with no filler. Purpose is front-loaded, the return value is summarized, and the critical sequencing instruction is stated at the end. 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 first-step analysis tool with no output schema and no annotations, the description covers the central facts: what it analyzes, what it returns, and when to call it. It could be slightly richer about mode semantics or preconditions, but those are already largely captured in the schema and this is otherwise sufficient 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 coverage is 100%, so the input schema already documents all three parameters (url, mode, codebase_path). The description adds no additional parameter-level meaning beyond the overall workflow, so it sits at the baseline 3 rather than higher.

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 ('Analyze') and resource ('a project's codebase'), and enumerates what is examined: structure, routes, forms, components, existing tests, and coverage gaps. It distinguishes itself from siblings like scan_page_elements (which is page-specific) by making the codebase-wide scope explicit and by positioning itself as the initial step.

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 instruction 'Call this first before any testing' provides explicit when-to-use guidance, establishing this tool as the entry point before scanning pages, executing scenarios, or generating reports. It does not explicitly list alternatives or state when not to use it, so it stops short of a full 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 must carry the behavioral disclosure. It reveals that the tool navigates, scans elements, and returns a screenshot, but it does not state whether it is read-only, whether navigation persists, or any limitations such as dynamic content handling. This is adequate but not rich.

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 with no unnecessary words. It front-loads the purpose, then describes the output, then gives the usage context. Every sentence contributes to agent understanding.

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 2-parameter tool with full schema coverage, the description covers purpose, output structure, and usage context. It lacks an output schema but describes the return values in sufficient detail. It does not mention how the authenticated parameter affects behavior, but that is a minor gap.

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

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

Schema coverage is 100%, so baseline is 3. The description adds no parameter-specific meaning beyond what the schema already provides for route and authenticated. No compensation is needed because the schema documents both parameters fully.

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 navigates to a page and discovers interactive elements, and lists the returned data (types, text, selectors, disabled state, screenshot). This is specific, but it does not explicitly contrast with siblings like take_screenshot or explore_page, leaving some potential overlap ambiguity.

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?

It explicitly says to use this to understand a page before deciding what to test, which gives a clear usage context. It does not mention when not to use it or name alternative tools, but the context is sufficient for selecting this tool in many cases.

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 behavioral burden. It does disclose the output shape ('prioritized, executable scenario objects with steps') and signals this is an analysis/suggestion tool rather than an execution one, but it does not explicitly state side effects or non-mutating behavior. This is adequate but not rich.

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, both necessary: the first defines purpose and output, the second provides usage context. It is front-loaded with the core function and avoids 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 1-parameter, no-output-schema suggestion tool, the description covers the key context: what inputs it considers, what it returns, and when to call it relative to scan_codebase. It is slightly light on explicit return-value detail, but the phrase 'prioritized, executable scenario objects with steps' gives enough shape for an agent to use 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 schema already documents the sole optional 'route' parameter with 100% coverage, including an example and default behavior. The description adds no additional parameter-level meaning, which aligns with the baseline of 3 when the schema carries the load.

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 a specific verb ('Analyze'), a clear resource ('codebase features, existing test coverage, and results from previous runs'), and a concrete deliverable ('concrete test scenarios... Returns prioritized, executable scenario objects with steps'). It distinguishes itself from siblings like scan_codebase and execute_scenario by framing itself as the suggestion step after analysis and before execution.

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 explicit sequencing guidance: 'Use this after scan_codebase to understand what testing is missing.' It makes the intended context clear, though it does not explicitly list exclusions or directly contrast with alternatives like get_coverage or execute_scenario.

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 does a good job by disclosing the key behavioral output: the screenshot is returned as a base64 data URI that the editor LLM can see. It does not cover failure modes or authentication quirks, but the observable behavior most relevant to an agent 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?

Two sentences with no wasted words. The action is front-loaded, followed by the output format and use case. 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?

The tool is simple, all three parameters are documented by the schema, and the description supplies the missing output explanation (base64 data URI). An agent has enough information to invoke it correctly and interpret 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?

Schema description coverage is 100%, so url, full_page, and authenticated are already fully documented. The description does not add parameter-specific detail or examples, so the baseline of 3 is appropriate.

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

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

The description uses a specific verb and resource: 'Navigate to a URL and take a screenshot.' It also names the output format (base64 data URI) and the intended use ('quick visual verification'), which clearly distinguishes it from analysis-oriented siblings like scan_page_elements and explore_page.

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 explicitly states when to use the tool: 'Use this for quick visual verification.' It provides clear context but does not name sibling alternatives or state when not to use it, so it misses the top-tier criterion of explicit exclusions.

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