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

No annotations exist, so the description carries the full burden. It adds meaningful behavioral detail: 'no embeddings needed', 'One Choice scores every candidate', and a 'Noul' check preventing an unsupported 'top hit'. It does not cover output format or error behavior, but for a read-style search tool the core operation is well disclosed.

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?

Description is two information-dense sentences plus a link and a use case. The opening sentence front-loads the main function. Some jargon ('One Choice', 'Noul', 'TypeSafe Jev') could be clearer, but nothing is extraneous.

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 moderately complex with a nested candidates array and no output schema. The description gives use context but does not explain return shape, how the 'Noul' result appears, or when to prefer jev_screen/jev_verify over jev_find. These gaps matter given the absence of an output schema.

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 mentions plain-language query and candidate count, but these largely repeat schema content. No additional parameter-level insight (e.g. top_k behavior, id conventions) is added 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?

States a specific verb+resource ('Rank candidates against a plain-language query') and adds a concrete use case ('which file/note/line covers X'). It is clearly a search/ranking tool, but it does not explicitly contrast itself with siblings jev_screen or jev_verify, so it misses the top score.

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

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

Provides a clear trigger phrase ('Use for which file/note/line covers X') and a capacity limit (up to 250 candidates). It does not explicitly say when not to use it or reference alternatives among the named siblings, so it falls short of full 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?

With no annotations, the description carries the full burden and does a good job: it explains the evaluation dimensions, the recommendation values (pass|review|block|skip), and the conditional relevance behavior. It does not explicitly state side-effect-free behavior or response structure beyond the recommendation list, but the judging nature is clear.

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

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

The description is two sentences, front-loads the core purpose, and includes the output contract without fluff. The reference to the cookbook pattern is useful and compact. 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?

Given four parameters, no annotations, and no output schema, the description provides enough for an agent to invoke the tool correctly: it states inputs, output categories, and conditional behavior. It could be more explicit about the probability output format or threshold semantics, but the schema already covers threshold parameters.

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 all four parameters clearly. The description adds some context by linking 'purpose' to the relevance judgment and 'skip' action, but it does not meaningfully supplement the parameter meanings beyond what the schema provides.

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

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

The description states a clear verb ('Judge'), a specific resource (fetched or external text), and the analysis dimensions (injection probability, substantive content, relevance). It does not explicitly differentiate from the sibling tools jev_verify and jev_find, so it misses the top score for sibling distinction.

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 clearly identifies when to use the tool: 'before an agent reads it.' It implies the guardrail context and references a cookbook pattern, giving solid situational context. However, it does not mention when not to use it or alternatives among the siblings.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly explains what the tool returns per claim—verdict, probability distribution, confidence, and auto/review status—and implies a confidence-threshold behavior through the output. It does not mention side effects or rate limits, but the verification behavior is a read-only-style computation and the output behavior is detailed enough for an agent to anticipate the result.

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 every one earns its place: core action, return format, and practical usage mapping. The most important information is front-loaded, and the writing is compact without sacrificing the behavioral detail an agent needs.

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 two required parameters, one optional threshold, and no output schema, the description covers both what the agent should pass and what it should expect back. The evidence parameter's ability to accept multiple items and map claims to evidence is handled partly by the schema and partly by the description, leaving only minor gaps around exact output formatting.

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 all three parameters, so the description does not need to repeat their mechanics. The description adds useful mapping examples ('reports, PR descriptions, or agent briefs' as claims; 'cited sources, diffs, or documents' as evidence), but it does not add meaning to auto_accept beyond the schema, giving it only modest added value at this coverage level.

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 ('Check each claim against provided evidence text') and a distinct resource ('TypeSafe Jev'), making the tool's core function unmistakable. It does not explicitly distinguish this from siblings jev_screen and jev_find, but the verification purpose and return categories are specific enough for an agent to separate it from those names.

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 concrete usage context: pass reports, PR descriptions, or agent briefs as claims, and cited sources, diffs, or documents as evidence. It does not explicitly state when not to use this tool or name alternatives, but the input examples provide clear practical guidance for selecting appropriate content.

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