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

No annotations exist, so the description carries the full disclosure burden. It does convey the substance of the operation (environment/dependency probing) and implicitly that it is a non-mutating diagnostic, but it says nothing about what a result looks like, whether it fails fast, how long it may take (Chrome detection can be slow), or whether missing dependencies raise or return a status. Useful but incomplete 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?

A single front-loaded sentence prefixed with 'Preflight:' that identifies the stage and then lists the checks. No filler, no redundancy, and the most important cue (when in the workflow it runs) comes first.

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

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

With no output schema, the description should explain what the agent gets back so it can interpret results and decide whether to proceed to probe_page. It lists what is checked but not what a successful or failed result looks like, leaving a real gap for a diagnostic tool whose entire value is its return payload.

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 takes zero parameters, so there is nothing for parameter documentation to add; the baseline for a no-param tool is 4. The description correctly implies no inputs are needed.

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

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

The description names a concrete action (preflight check) and enumerates the three things verified: Node, the bundled probe script, and Chrome detection. That is far more informative than a tautology, and it is distinguishable from probe_page. It stops short of 5 only because 'check' is a slightly loose verb and the scope of each check (version threshold? install path?) is unspecified.

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

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

The word 'Preflight' strongly implies this runs before the real work, which in context means before probe_page, but that ordering is never stated explicitly and no alternatives or when-not conditions are given. Usage is inferable rather than spelled out.

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 well: it discloses that viewport emulation persists regardless of the Chrome window size, that login runs a JSON POST inside the page context and therefore carries httpOnly cookies, and that the target app must expose a JSON login API. It stops short of stating whether the tool is otherwise side-effect-free, whether the browser session is shared, or any concurrency/timeout failure 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 purpose is front-loaded in one sentence, then parameters are enumerated in a scannable list with the return shape last. The measure_js example is long but earns its space by making a non-obvious parameter usable; there is only minor redundancy in the viewport bullet.

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 9-parameter browser automation tool with no annotations and no output schema, the description supplies the return shape ({ok, origin, viewport, login_status, measurements, png_base64}), the login mechanics, and the measurement example. It is nearly complete; only safety/side-effect profile and failure modes are absent.

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

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

Schema description coverage is 0% across 9 parameters, so the description must compensate entirely and it does: every parameter is documented with type-appropriate semantics, defaults (login_url=/api/auth/login, timeout 30000), the %u/%p placeholder contract in login_body, and a full working measure_js IIFE example. This is far beyond what the bare schema conveys.

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 pair and resource: 'Measure and/or screenshot a page in real local Chrome.' The 'real local Chrome' qualifier distinguishes it from headless/HTTP fetching and from the sibling probe_available, which by its name only checks reachability. An agent knows immediately what this tool produces (measurements and/or a PNG).

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

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

Usage is implied by the worked measure_js example (measuring one element's real rendered text width), which conveys the intended use case without stating when to prefer this over probe_available or when not to use it. There are no explicit exclusions or prerequisites beyond the login-requires-JSON-API note. Adequate but with a clear routing gap.

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