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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does mention the return format (choice, confidence, probability distribution), which is useful, but it omits critical context such as whether the tool has side effects, requires authentication (api_key is a parameter), has rate limits, or is read-only. The behavior is only partially described.

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 long, with the purpose front-loaded and no unnecessary detail. It efficiently conveys the core action and expected output. Slight deduction for not providing any usage context, but the structure itself is clean and focused.

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 6 parameters, nested objects (state, criteria), and an output schema, the description is incomplete. It does not explain how to structure state or criteria, when to override the model, or how to supply the api_key. While the output schema covers return values, the input construction is left ambiguous, so an agent would struggle 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 0%, so the description must compensate by explaining parameters. It references a 'defined rubric' (likely criteria) and the act of categorizing, but does not explain the roles of state, instructions, model, api_key, or question_id. This leaves most parameters undocumented, making it hard for an agent to construct valid arguments.

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 routes a single Choice question to Jev and performs categorization/selection from a rubric, with explicit output (choice, confidence, probability distribution). However, it does not explicitly differentiate from sibling tools like jev_score or jev_evaluate, leaving the distinction implied by the name rather than stated.

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 guidance on when to use this tool versus its siblings. The description only says what it does, not when it is appropriate or when to avoid it. No alternatives or exclusions are mentioned, so an agent cannot decide between jev_choice and similar 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 burden. It discloses parallelism, input flexibility, and return contents (calibrated probabilities, confidence scores, token usage), but does not address safety/mutation, auth, or error behavior. Parallel evaluation and return details are useful but not a full behavioral profile.

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?

Three short sentences front-load the router role, the operation, the input types, and the output. Every sentence adds information; no filler or repetition of schema titles.

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 gives a competent overview for selection and basic invocation, but the tool is a multi-question router with nested input and no annotations. It omits how individual typed questions are structured and does not mention credential/model parameters, so an agent is not fully equipped 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 0%, so the description must add meaning. It clarifies that 'state' may be unstructured or structured and that 'questions' are typed evaluation questions, but it does not explain 'model' or 'api_key' or the expected shape of a question object.

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 ('evaluates'), a resource ('unstructured or structured state'), and a distinguishing scope ('multiple typed questions (noul, choice, score) in parallel'). This clearly separates it from the single-question sibling 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?

It is identified as the primary proxy router, and 'multiple' plus 'in parallel' signal when to prefer it over the individual typed tools. It does not explicitly state when not to use it or name those tools as alternatives, so it stops 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.

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

With no annotations provided, the description carries the full burden and does disclose the key output behavior: a calibrated probability between 0.0 and 1.0. It does not, however, mention that the api_key parameter implies an external service call — with attendant auth, cost, and network-failure implications — nor does it disclose error behavior, latency, or whether the call is idempotent.

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 tightly-worded sentence front-loads the purpose and follows with the return contract and use case. Every clause earns its place and there is zero 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?

Although an output schema exists (so return values need not be spelled out), the input side is badly under-specified: two required polymorphic parameters with no guidance, no annotation safety profile, and sibling tools that could be confused with it. An agent receiving this definition cannot confidently construct a correct call to state and instructions.

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%, so the description must compensate for six undocumented parameters — but it explains none of them. The required params state and instructions are polymorphic (string/object/array) and the agent is left guessing what content belongs in each; criteria, model, question_id, and api_key are likewise unexplained. The description only covers tool-level purpose, not how to populate the arguments.

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: 'Route a single Noul (yes/no) question to Jev' and defines the output precisely as 'the calibrated probability (0.0 to 1.0) that the answer is yes.' This clearly distinguishes it from the sibling set (evaluate/choice/score) by its yes/no-with-probability nature, though it never names the siblings explicitly.

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 'suitable for confidence gating and binary routing' gives useful implied usage context, signaling this is a decision/threshold tool rather than a free-form evaluation. However, it offers no explicit when-to-use versus when-not-to-use guidance against the siblings jev_evaluate, jev_choice, and jev_score, leaving the selection logic 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 of behavioral disclosure. It does add useful behavior: the input is rated along an ordered rubric of at least two descriptive levels datapoints and the result includes a weighted score and confidence. However, it omits important behavioral context such as whether an API key is required, how the weighting is determined, what confidence means, and failure/error 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 two sentences with no filler. Every phrase earns its place: the resource, the routing action, the rubric constraint, and the returned values. It is tight, front-loaded, and quickly scannable.

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?

Although an output schema is presentasi and the return values are partially described, the description is incomplete for a tool with six parameters and three required fields. The agent still lacks enough detail to construct valid calls, especially for the required instructions and criteria, and receives no guidance about sibling distinctions. The overall context is too sparse for reliable 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 0%, so the description must compensate for six undocumented parameters. The terms 'input' and 'ordered rubric' vaguely map to state and criteria, but neither required parameters such as instructions and criteria nor optional ones like model, api_key, and question_id are explicitly explained. The description adds only a thin layer of meaning beyond the raw 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?

The description uses specific verbs ('Route', 'Rates') and identifies a concrete resource ('a single Score question'), a scoring mechanism ('ordered rubric'), and the output ('weighted score and confidence'). It implicitly separates this tool from siblings like jev_choice and jev_evaluate by focusing on rubric-based scoring, but it never names or contrasts them explicitly.

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 'a single Score question' hints at a narrow use case, but the description gives no explicit guidance on when to choose this tool over jev_evaluate, jev_choice, or jev_noul. It does not state conditions, exclusions, or alternatives, leaving the agent to infer the appropriate context.

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