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

No annotations exist, so the description carries the full burden, and it does disclose two meaningful traits: the decision is 'offline' and 'deterministic', which implies a pure, side-effect-free computation with no external calls. However it says nothing about the return shape, whether the result is advisory or binding, or why the schema exposes seven parameters while the text claims five factors.

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 with no filler, and the four candidate outputs are placed where they are most useful. It is dense with internal jargon (Layer-0 Reflex, System-1/System-2) that is never unpacked, which costs a little clarity but not 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?

For a seven-parameter, no-annotation, no-output-schema tool, the description is only marginally complete. Enumerating the four possible recommendations partially substitutes for an output schema, but the description never explains how the numeric inputs combine or what the caller receives, leaving real ambiguity for an agent that must act on 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 71%, so the schema documents most inputs (risk, confidence, reversible, plus defaults like cost_pressure and expected_value), and the description adds no parameter-level meaning at all. The '5-factor' phrasing also conflicts with the seven declared parameters, which could confuse an agent about which inputs actually drive the decision.

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 and resource ('Run the offline deterministic 5-factor Meta-Controller') and enumerates the four possible recommendation outputs, which tells an agent what the tool produces. It does not distinguish itself from the sibling jev_route, which plausibly also makes routing decisions, so sibling differentiation is missing.

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 no when-to-use or when-not-to-use guidance. With siblings jev_route, jev_guard, jev_verify and jev_rerank in the same family, an agent has no stated condition for choosing arbitration over routing or guarding; usage must be inferred purely from the name.

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 whole behavioral burden. It does disclose two useful traits — the selector runs offline and needs no configuration or manual grouping — which tells the agent this is a cheap, self-contained heuristic rather than a network-backed service. It says nothing about determinism, whether the recommendation is advisory or binding, or the shape of the result, which matters since there is no output schema.

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 with the capability stated immediately and no filler. Minor redundancy: "zero-config" and "without manual grouping" express nearly the same idea.

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 low-complexity with two well-documented params, and the description names the three recommendation axes. However, with no output schema, it should describe the return value's structure (a single tier triple? a per-axis object?) and whether confidence or rationale is included. That gap keeps it merely adequate.

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

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

Schema coverage is 100%, so context_chars and task_description are already documented in the schema. The description hints at the reasoning_effort enum it produces (low/medium/high) but adds no guidance on how task_description influences the recommendation or how context_chars is used. Baseline 3 applies when the schema does the heavy lifting.

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 pairs a specific verb ("recommends") with three concrete outputs: model tier, reasoning_effort, and context tier. That is far more informative than the opaque name jev_autopilot. It stops short of distinguishing itself from the sibling jev_route, leaving the agent to guess which is the general router and which is the zero-config autopilot.

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?

"Zero-config" and "without manual grouping" imply the use case: call this when you don't already know the tiering and don't want to specify grouping yourself. No explicit when-not conditions, prerequisites, or named alternatives (e.g., jev_route) are given, so routing between siblings is left to inference.

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

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

With no annotations, the description carries the full burden and does disclose key traits: the run is offline (no network), deterministic (reproducible results), and returns both accuracy and timing metrics. Gaps remain — it doesn't say whether the run writes any files, how long it typically takes, or what happens if input_path is missing — but the core behavioral profile is conveyed.

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 that states action, key properties, and return values with no filler or redundancy.

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

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

There is no output schema, so the description must signal what comes back; it does so ('accuracy and timing metrics'). Combined with the offline/deterministic qualifiers, an agent has enough to call it correctly, though runtime expectations and side effects are unstated.

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?

Only one parameter and schema coverage is 100%, so the schema already documents input_path as the JSONL fixture path including its default. The description adds no extra meaning (e.g., accepted formats, whether relative or absolute paths are expected), matching the baseline for well-covered schemas.

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 ('Run') and resource ('offline deterministic Layer-0 JSONL replay benchmark suite') and adds the return payload ('accuracy and timing metrics'). None of the siblings (jev_autopilot, jev_verify, etc.) overlap with a benchmark runner, but the description never names or contrasts an alternative, so it stops short of explicit sibling differentiation.

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 only implied: an agent can infer this is the tool to invoke when it wants to measure Layer-0 replay performance. There is no explicit when-to-use, when-not-to-use, or prerequisite statement (e.g., whether the corpus must exist, whether results are comparable across runs).

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 behavioral burden. It hints at a security gate ('Layer-0 DLP/SSRF pre-screening') and 'zero-popup' launch behavior, but never says what happens when screening blocks a request, whether errors are returned or calls silently fail, or that some CDP methods (DOM.setFileInputFiles, Page.navigate) mutate browser state destructively. Undefined jargon like 'Layer-0' adds no actionable 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?

It is a single short sentence with no filler, which is good, but it is packed with stacked compound jargon ('Layer-0 DLP/SSRF', 'M144+', 'DevToolsActivePort') and leads with an adjective rather than the primary action. Dense does not equal front-loaded; the core action is never stated.

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?

This is a complex tool with a nested free-form 'params' object, a CDP method dispatcher, security gating, and no output schema or annotations. The description leaves open what the tool returns (a port path, a screening verdict, a CDP result?), how blocked calls surface, and what state changes are possible — significant gaps for an agent to call 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 each of the four parameters is documented in the schema itself, so the baseline is 3. The description adds nothing about parameters — notably it never explains the relationship between 'params', 'method', and the pre-screening step, nor what 'target_url' is actually screened for.

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 concrete concepts (DevToolsActivePort discovery, DLP/SSRF pre-screening, CDP automation) that identify the resource area, but it is a noun phrase with no clear verb and never states that the tool actually dispatches the CDP call implied by the 'method' parameter. An agent cannot confidently tell whether this only discovers a port and screens a URL or also executes Runtime.evaluate/DOM.setFileInputFiles against the browser.

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

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

There is no when-to-use guidance at all: nothing distinguishes this from siblings such as jev_guard, jev_route, or jev_autopilot, and no preconditions (a live Chrome instance, an authenticated session) are stated. The only implied context is 'live browser CDP automation', which is too thin to route 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, the description carries the full behavioral burden. It usefully discloses that the result is advisory and non-intercepting, and that evaluation is layered (deterministic regex then probabilistic unless reflex_only), plus the offline/runtime implication of reflex_only. It stops short of describing what the screening produces (score, verdict, findings) or latency/cost characteristics, which is a meaningful gap for a tool with no output schema.

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 core purpose and capped by the non-interception guarantee. No filler, and the parenthetical mode switch is placed where it is needed.

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 annotations and no output schema, the description covers the input side and the safety posture but never explains what the agent receives back or how to act on it. The core purpose is clear, yet the informational contract of the call is left implicit.

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 three parameters are already documented, and the description largely mirrors that (reflex_only's offline meaning is already in the schema). It adds no syntax, format, or example for 'command' or 'context', so it sits at 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?

States a specific verb and resource ('risk screening for a shell command') and specifies the two evaluation layers, which is more than a restatement of the name. It does not explicitly differentiate itself from siblings like jev_route or jev_arbitrate, so an agent still has to infer the boundary.

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

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

The phrase 'advisory risk screening' and 'Does not intercept host execution' imply a pre-flight, non-blocking checking context, and the reflex_only clause hints at an offline mode. However, there is no explicit when-to-use/when-not guidance and no reference to the sibling tools for alternative needs.

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 on its own. It does usefully reveal batching granularity (batches of 12), a hard input ceiling (96 items), and a runtime dependency — genuine operational context — but says nothing about ordering of results, determinism, scoring scale, or what happens when the 96-item limit is exceeded.

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 with no filler: verb, resource, throughput limit, batching behavior, and runtime prerequisite. 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?

There is no output schema and no annotations, so the description should explain the shape of the result (ranked list with scores? ordering? top_k default behavior). It covers input constraints well but leaves the return-value contract entirely to inference.

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

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

Schema description coverage is 100%, so all three parameters are already documented, establishing a baseline of 3. The description adds the 96-item/batch-of-12 constraint relevant to the items array, but adds no new meaning for 'query' or 'top_k' beyond the schema.

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

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

Clear specific verb (rank) and resource (candidate text items against a query), plus concrete limits (up to 96 items, batches of 12). It does not differentiate itself from the sibling tools (jev_guard, jev_route, jev_verify, jev_arbitrate), but the names are distinct enough that confusion is unlikely.

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 supplies a precondition ('requires JevBridge runtime') which constrains applicability, but gives no explicit when-to-use/when-not guidance or alternative tooling for ranking-like tasks. Usage is only implied by the verb and the prerequisite.

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 behavioral burden. It discloses that output is advisory (non-binding) and requires a configured runtime, but omits error behavior, return format, and whether the operation is read-only.

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

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

A single, front-loaded sentence with the prerequisite parenthetically placed. It is tight and wastes no words.

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

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

For a simple two-parameter advisory tool with no output schema, the description states the recommendation types but omits return format specifics and sibling-routing context. It is adequate but leaves gaps in what an agent might need for correct invocation.

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

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

Schema description coverage is 100%, so the schema already documents both parameters fully. The description adds no additional parameter semantics, which is acceptable given 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?

States a specific verb ('Provide') and resources ('task complexity and model tier recommendation'), making it clear what the tool returns. However, it does not explicitly differentiate from siblings like jev_guard or jev_verify beyond the distinct action name.

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 only provides a prerequisite ('requires configured JevBridge runtime') and offers no guidance on when to use this tool versus alternatives such as jev_rerank or jev_arbitrate. Usage context must be inferred entirely.

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 disclosure burden, and it does add value by stating the tool is purely analytical (does not run tests, does not read diffs) and has a runtime dependency. It still omits whether the operation mutates anything, what happens on incomplete input, and what the review output looks like.

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 packs the action, the inputs, the output goal, and the key limitations without redundancy. The parenthetical is dense but does not wander.

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 3-parameter analysis tool with no annotations and no output schema, the definition covers inputs and scope limits but leaves the response shape (what an 'evidence gap' report contains) and failure/edge behavior unexplained. Adequate but with visible gaps.

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

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

Schema description coverage is 100% and each of the three parameters is documented in the schema, so the schema does the heavy lifting. The description restates the same three inputs without adding format, size, or content expectations beyond what is already structured.

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?

Names a specific verb (review) and the exact inputs it consumes (task specification, change summary, test logs), plus the goal (detect evidence gaps). It is clear what the tool does, though it never contrasts itself with the sibling jev_* tools, so differentiation relies on inference.

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 parenthetical gives real boundary conditions — it does not run tests or read git diffs, and it requires a JevBridge runtime — which implies when the caller must supply data themselves. However, it offers no guidance on when to choose this over jev_guard, jev_route, jev_rerank, or jev_arbitrate, leaving routing to the agent.

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