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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds substantial behavioral context beyond that: it never returns prose or reasoning, it does not decide for you, it returns probabilities and confidence, low confidence can mean two options are both acceptable, noul near 0.5 means 'cannot tell' not 'medium', and flagged answers must not be acted on as settled. This is rich, non-obvious behavior that an agent must know before invoking. It loses one point only because it does not explicitly discuss rate limits or latency, which are minor for a read-only judgment 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 long but every section earns its place: purpose, when-to-use, question types, result interpretation, and escalation behavior. It is front-loaded with the core purpose and the 'never returns prose' constraint. It loses one point because the result-reading section is dense and could be tightened; some sentences (e.g., the escalation paragraph) repeat the 'do not act on flagged answers' idea in slightly different forms. Still, for a tool with three question types and a rich output, this length is justified.

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 (3 question types, nested question objects, thresholds, rich output schema), the description is remarkably complete. It explains the three question types, how to interpret each result field, what the certainty/verdict/escalation fields mean, and how to handle flagged answers. The output schema exists, so return values need not be re-explained. The only minor gap is no explicit mention of rate limits or cost, but those are not essential for correct invocation. For a tool this complex, this is a model definition.

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 real value beyond the schema: it explains how to read the result fields (choice/probabilities/confidence, noul, score, certainty, verdict, needs_escalation), which is not in the input schema. It also gives guidance on state ('pass actual content, not your summary') and questions ('put every independent question in the same call'). However, the description does not add much about the thresholds parameter beyond what the schema already says, and the question-type semantics are mostly carried by the schema's detailed per-type descriptions. So it is a solid 4, not a 5.

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 opens with a specific verb and resource ('Get fast, calibrated judgments from the Jev model at a decision point') and immediately contrasts with free-text guessing. It names the three question types (choice, noul, score) and explicitly says what it never returns (prose/reasoning) and what it does not do (decide for you). This clearly distinguishes it from siblings like review_files or verify_claim, which are about reviewing or verifying rather than producing calibrated probabilistic judgments.

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 'WHEN TO USE' section is explicit and actionable: it lists concrete decision scenarios (pick between known options, check a condition, grade on a dimension) and gives a 'Do not use' list (open-ended reasoning, arithmetic, exact lookups, plain code/tool-settable things). This is exactly the when/when-not guidance an agent needs to route correctly 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?

Annotations already declare readOnlyHint and openWorldHint, and the description adds meaningful behavioral context: each candidate is judged in its own request seeing only 'context' and its own 'content', candidates are never compared against each other, and low-certainty pairs are surfaced via 'needs_escalation'. It also explains how ties are handled and warns not to trust the order when 'top_tied' is true.

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 long but tightly organized with clear sections (WHEN TO USE, WHAT TO PASS, HOW TO READ THE RESULT) and front-loads the core instruction. Each section earns its place by addressing a distinct decision the agent must make: when to call, what to pass, and how to interpret the response.

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 the rich output schema, the description adds essential interpretation details: how to read 'composite', 'top_margin', 'top_tied', and 'needs_escalation', plus a warning about weak candidates where the top one still has a low composite. For a complex tool with nested parameters and multiple siblings, nothing needed for correct invocation or result interpretation is 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?

Schema coverage is 60%, so the description needs to add meaning, and it does. It clarifies that 'context' is the shared basis for judging all candidates, that each candidate's 'content' is judged only against that context, that dimensions must be independent ('split good and cheap into two dimensions'), and that levels are ordered worst to best with cost-like dimensions having the most expensive case first.

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 opens with a specific verb and resource: 'Rank 2-30 candidates... by grading each one on several described dimensions'. It also states the output contract ('Returns numbers only, never prose') and explicitly distinguishes itself from judge, making the tool's purpose and scope immediately clear.

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

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

The 'WHEN TO USE' section states the exact condition for selection ('you have a shortlist and the better choice depends on more than one factor') and names the alternative for the opposite case: 'For picking one option on a single question, use judge with a choice question instead.' This is explicit when-to-use and when-not-to-use 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?

Beyond the readOnlyHint/openWorldHint annotations, it discloses that the tool reads nothing from disk, runs no git, sends patches to the TypeSafe API, does not compile or run tests, and explains that findings are leads requiring manual confirmation. This adds substantial trust and safety context.

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?

Well-structured with headings, purpose front-loaded, and no filler. The length is considerable but justified by the tool's complexity; every section earns its place, though it could be slightly tightened.

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?

Even with an output schema present, the description explains how to interpret findings, uncertainty, unresolved_signals, failed_requests, and cost/performance. It covers edge cases like empty findings and screen failures, making it fully complete 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?

The description explains scope decisions (choose base, include uncommitted/untracked work), what to exclude (generated files, lock files, sensitive data), the exact patch format required (unified diff as git printed), and the role of test_files as evidence whose omission fires test-gap screens. This goes far beyond the schema's field 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 a specific verb+resource: 'Run the staged Jev review over a diff you supply and return a structured report, never prose.' It explicitly names alternatives (judge, verify_claim) for different use cases, distinguishing it from siblings.

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?

Has a dedicated WHEN TO USE section listing concrete scenarios (before committing, before opening/merging a PR, when asked to review a branch) and explicitly directs to judge for single-patch judgments and verify_claim for checking findings. This is clear and complete.

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 already declare readOnlyHint and openWorldHint, and the description reinforces them by noting the tool reads nothing from disk, judges exactly the text passed, and does not compile, run tests, or follow calls. It also discloses cost, retry/failure behavior, and the meaning of empty findings, which is materially useful and does not contradict the annotations.

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 long but well structured with clear headings and a front-loaded purpose. Nearly every section carries operational value, though a few points are repeated and the output-field walkthrough goes beyond what a shorter definition would need, preventing a perfect conciseness score.

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

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

For a tool with nested parameters, a rich output schema, and several sibling tools, the description is unusually complete: it covers when to use it, what to pass, cost, how to interpret findings, failure stages, and the open-world limitation. An agent has enough context to select the tool, invoke it correctly, and 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 coverage is 100%, so the schema already documents the parameters; the description adds real value for files and test_files: full unmodified content, no trimming or line numbers, choose only relevant files, leave out generated/vendored/confidential code, and test_files are evidence only for the test-gap dimension. thresholds is not elaborated in the prose, but the schema description covers it adequately.

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 specific action: run the staged Jev review over supplied source files and return a structured report, never prose. It clarifies the scope (whether an issue exists in code as it stands, not whether a change introduced one) and differentiates itself from review_changes, which handles diffs.

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 an explicit WHEN TO USE section stating this tool is for auditing files you did not just change or for finding where to start reading unfamiliar code, and that review_changes should be used for work in progress or a branch. It also gives exclusion guidance: pass files at the center of the flow, not a whole repository, and omit generated, vendored, or sensitive code.

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 goes far beyond the readOnlyHint/openWorldHint annotations by disclosing exact verdict semantics ('verified', 'contradicted', 'unsupported', 'fabricated'), stating that fabricated verdicts use string matching with no model call, that probabilities are null in that case, and that needs_escalation means a verdict is not settled. It also warns that the tool cannot notice omitted evidence, reinforcing the openWorldHint.

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 long but tightly organized with clear section headers ('WHEN TO USE', 'WHAT TO PASS', 'HOW TO READ THE RESULT'). Every paragraph contributes new, decision-relevant detail, and the most important guidance is front-loaded. Nothing feels redundant 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 nested claim objects, an evidence union type, tunable thresholds, and a rich result, the description covers all the tricky parts: claim splitting, quote handling, verdict interpretation, low-certainty escalation, and the fact that evidence scope bounds the conclusion. The presence of an output schema reduces the need to document return values, but the description adds even more clarity.

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?

Even though the schema already documents each parameter, the description adds operational meaning: evidence must be raw material, not a summary; claims should be split into separate entries; quote is checked by exact string match; thresholds can be tightened when the cost of a wrong answer is high. This makes the abstract schema actionable.

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 action ('Check whether supplied evidence actually supports claims') and a clear resource/scope ('claims you are about to state or act on'). It also immediately distinguishes the tool's output format ('verdict and probabilities per claim, never prose'), which separates it from vague review/judgment tools among siblings.

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 'WHEN TO USE' section is explicit and practical, listing concrete moments such as 'before reporting work as complete', 'before citing a source', and 'before building the next step'. 'WHAT TO PASS' clarifies what counts as evidence versus claims. It does not explicitly name alternative tools or say when not to use it, so it falls just 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.