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

There are no annotations, so the description carries the full behavioral disclosure burden. It explains the three question modes with their return shapes, explicitly warns that confidence is a separate signal from the top probability, and describes the high-level return fields (upstream model, answers, usage, latency_ms). This goes well beyond a minimal description.

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?

Though longer than average, the description is dense and well-structured with bullets and code-formatted types. The main purpose is front-loaded, followed by parameter semantics, a key caveat, and return information. Every sentence adds necessary value, and the length is justified by the tool's complexity.

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 sparse schema and lack of annotations, the description is remarkably complete. It covers all valid question types, the meaning of each output field, the confidence caveat, and the overall response structure. The output schema exists, but the description adds essential interpretation details and usage nuances, leaving no major invocation 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?

The input schema has 0% description coverage, and the description fully compensates. It defines 'state' as any JSON value and documents the 'questions' object with concrete formats for choice, score, and noul questions, including output fields. An agent can construct a valid call without needing additional parameter documentation.

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 ('Evaluate') and a clear resource ('a decision state using Kev's pointer-head model'). It also states that several questions may be packed into one call and enumerates the three question types, so an agent can immediately distinguish this from sibling tools like kev_list_models or kev_permute.

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 clear context: use this tool when you need to evaluate a decision state with Kev's pointer-head model, and it even advises that multiple questions can be packed into a single call. However, it does not explicitly mention sibling alternatives or state when not to use this tool, so it lacks full exclusionary 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 provided, the description carries the full burden of behavioral disclosure. It does convey that the tool lists models and metadata, implying a read-only operation, but it does not explicitly state side-effect safety, authentication requirements, or list behavior (e.g., pagination, ordering). This is adequate but not rich.

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 a single concise sentence that front-loads the core action ('List Kev models') and then adds valuable metadata examples. Every word earns its place, with no redundancy 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?

Given zero parameters, an existing output schema, and a simple list operation, the description is nearly complete. It covers what is returned at a high level and would allow an agent to invoke the tool correctly, though it could mention output shape or pagination if applicable.

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 has zero parameters and the schema is empty, so the baseline for 0 parameters is 4. The description does not need to explain parameter behavior since none exist; it appropriately focuses on the tool's action.

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 a specific verb ('List') and resource ('Kev models'), and adds concrete metadata examples (device, temperature, prefix-cache statistics). This distinctly differentiates it from the sibling tools (kev_evaluate, kev_permute, kev_separate), which suggest different operations.

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 provides no guidance on when to use this tool versus alternatives like kev_evaluate, kev_permute, or kev_separate. There is no explicit 'when to use' or 'when not to use' context, leaving the agent to infer from the tool name alone.

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. It discloses key constraints: state can be any JSON value, questions must contain exactly one choice question, n_perm is 1-64, and seed controls reproducibility. However, it does not describe what the tool returns, whether it mutates state, or how errors/invalid input are handled.

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 compact and front-loaded with the main purpose. The parameter constraints are packed into two later sentences without unnecessary filler. It could be slightly more structured, but 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 that an output schema exists and the description fully explains the parameters and input constraints, an agent has enough to invoke the tool correctly. The only real gap is lack of explicit behavioral details about output/error cases, but the output schema mitigates that.

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%, but the description compensates thoroughly. It explains that state accepts any JSON value, questions must be an object with exactly one choice question of the specified shape, question must be that question's key, n_perm is bounded 1-64, and seed controls reproducibility. This gives meaningful semantics for all five parameters.

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 a specific verb ('Evaluate') and resource ('one Choice question under multiple option orders') and states the goal ('check order sensitivity'). It clearly indicates this is a specialization of evaluation, though it does not explicitly name sibling alternatives like kev_evaluate.

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: this tool exists for testing order sensitivity of a Choice question. However, the description does not explicitly say when to prefer this over kev_evaluate or kev_separate, nor does it state any exclusions, so the agent must infer the boundary from the sibling names.

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. It discloses that thresholds should be based on probabilities, not confidence, and explains the state format. However, it does not explicitly state whether the tool is read-only or has side effects, nor does it describe the return format. The evaluation nature implies no mutation, but this is not stated. Moderate behavioral 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?

The description is two concise paragraphs. The primary purpose is front-loaded, followed by the parameter specifics. It avoids fluff and every sentence adds value. It could be slightly more structured (e.g., bullet points), but it remains efficient and readable.

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 (nested question objects, multiple types, threshold guidance) and the absence of annotations, the description covers the essential usage details. An output schema exists, so return values need not be described. It explains how to construct questions and the key behavioral rule about thresholds. Minor gaps like error handling or explicit read-only status are acceptable given the output schema and sibling context.

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 0% and the schema is generic (state with no type, questions as arbitrary object). The description thoroughly defines state as any JSON value and details the exact structure of questions, including types (choice, score, noul) and criteria formats. This provides essential meaning that the schema completely lacks.

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's function: evaluate each question independently against the same state, for a packed-vs-separate comparison. It names a specific verb (evaluate), resource (questions against state), and the distinguishing purpose (separate comparison). This differentiates it from siblings like kev_evaluate without ambiguity.

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 context by mentioning 'packed-vs-separate comparison' and explicitly links the questions format to kev_evaluate, implying when this variant is appropriate. It lacks an explicit exclusion ('use this instead of X when...'), but the purpose statement effectively routes an agent to the correct tool.

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