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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'evaluates' commands and returns a policy, but it does not disclose whether the tool itself executes commands, makes external network calls, or has side effects. It also does not mention authentication requirements, rate limits, or any destructive potential of the tool itself. This is a significant gap for a safety-related 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 three sentences long and front-loaded with the core purpose. Each sentence adds value: purpose, evaluation method, and output policy. There is no redundant or filler content, and it is appropriately concise for 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?

The description lists the three possible policy outcomes, which partially covers the return value, but there is no output schema to elaborate on additional fields like confidence scores or reasons. It does not explain the meaning of Noul, Score, and Choice, nor does it describe error behavior or edge cases (e.g., empty command). For a tool with five parameters and no output schema, the description is adequate but not fully complete.

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% (all five parameters have descriptions in the input schema). The description adds no additional parameter-specific meaning beyond what the schema already provides. It mentions the calibration method (Noul, Score, Choice) but does not map these to parameters. Since the schema covers all parameters, a baseline of 3 is appropriate.

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 evaluates terminal/shell command safety and returns a policy decision. The verb 'evaluates' with the resource 'terminal/shell command safety' is specific, and the three possible policy outcomes (ALLOW_AUTONOMOUS, REQUIRE_HUMAN_APPROVAL, DENY_DESTRUCTIVE) clarify its purpose. However, it does not explicitly differentiate itself from sibling tools like evaluate_decision or jevguard_evaluate, which may have overlapping evaluation purposes.

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 context that this is for autonomous agents, but it does not state when to use this tool versus alternatives or when not to use it. There are no explicit exclusions or alternative recommendations. The usage is implied from the purpose rather than stated, which aligns with a score of 3.

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 meaningfully discloses hidden behavior: automatic injection of UNRESOLVED_OR_OTHER and calibration of probability dispersion. This goes beyond what the schema reveals, though it does not describe result format or side effects.

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 concise sentences with no filler. The purpose is front-loaded and the behavioral details are presented efficiently, making every sentence earn 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?

The description covers the core purpose and automatic behaviors, but with no output schema and no usage guidance, an agent still lacks clarity on what the tool returns or when to prefer it over related tools. It is adequate but not complete.

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 does not add parameter-specific details beyond the schema; it only reinforces the 'simple list of options' aspect, which is already covered by the options parameter description.

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 ('Evaluates') and resource ('architectural and implementation decisions'), and clarifies the input style ('simple list of options'). It does not explicitly differentiate from sibling tools like evaluate_command_safety or jevguard_evaluate, so it falls short of a 5.

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 choose this tool over its siblings, no exclusions, and no alternative tool mentions. 'Simple list of options' only implies a use condition; it does not establish when this tool is appropriate.

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?

There are no annotations, so the description carries the full behavioral disclosure burden. It transparently explains the computation, masking of volatile keys, and deterministic caching intent, but it does not state the return format, whether the model parameter participates in the fingerprint, or any error/side-effect behavior. For a pure calculation tool this is acceptable but incomplete.

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 dense sentence that front-loads the core action and purpose. Every phrase contributes meaning: the algorithm, the inputs, the masking behavior, and the intended caching benefit. 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?

Given the moderate complexity of five parameters and nested objects, the description gives enough context to understand why the tool exists and roughly how it behaves. Since there is no output schema, a note about the exact return format would improve completeness, but 'Calculates a canonical SHA-256 fingerprint' sufficiently implies the output.

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%, giving a baseline of 3. The description adds meaning by explaining that ignore_keys masks volatile keys such as timestamp, trace_id, and request_id, and by framing the overall purpose of the parameters. It does not mention the model parameter's role in the fingerprint, but still adds value 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?

The description names a specific verb ('Calculates') and resource ('canonical SHA-256 fingerprint from state and questions'), and adds distinguishing details like volatile key masking. It is clearly distinct from the evaluation/safety sibling tools, so an agent can tell what this tool is for without opening the schema.

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 'for 0-token deterministic caching' gives a clear intended use context, which helps an agent decide when to invoke this tool. However, it does not explicitly mention alternatives, when not to use it, or how it relates to sibling evaluation 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 provided, the description carries the full burden of behavioral disclosure. It reveals the internal decision logic (thresholds for top_prob and dispersion_gap) and implies a non-mutating evaluation, but it does not state whether the tool returns a report, modifies state, or requires specific permissions. This is a meaningful but not fatal gap.

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 zero filler. Every phrase contributes: the verb, the resource, and the two key detection criteria. It is compact and immediately 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?

The tool has four parameters, a nested object, no annotations, and no output schema, so the description should compensate by explaining return values and side effects. It covers the evaluation logic but omits what the tool returns, whether it is read-only, and the role of noul_uncertainty_margin. An agent would not know what to expect from invoking it.

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 baseline is 3. The description adds value by explicitly linking min_top_prob to 'top_prob < 0.40' and min_dispersion_gap to flat distributions, clarifying the thresholds' roles. It does not mention noul_uncertainty_margin, but the schema already documents that parameter 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 clearly identifies a specific verb ('Evaluates') and resource ('probability distributions across answers'), and specifies the purpose (identify ambiguity, low confidence, flat distributions). However, it does not differentiate from sibling 'jevguard_evaluate', so it stops short of full 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 usage context is implied: use this tool when you need to assess answer distributions for ambiguity or low confidence. There is no explicit when/when-not guidance or mention of alternatives like jevguard_evaluate, so it lacks the explicit routing that a 5 would require.

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 behavioral disclosure burden and does add meaningful traits: deterministic execution, closed-world escape injection, certainty calibration, and 0-token caching. However, it does not disclose side effects, mutation risk, required permissions, or output behavior, leaving significant behavioral context unstated.

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 dense sentence that fits a surprising amount of behavioral and scoping information: deterministic pipeline, four internal stages, and caching behavior. There is no filler or repetition.

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 solid high-level picture and all parameters are schema-documented, but there is no output schema and no description of what the evaluation returns or how results should be interpreted. For a complex six-parameter tool, that is a meaningful gap, yet the core invocation details are present.

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 without needing extensive parameter explanation. The description adds conceptual context that maps pipeline stages to parameters such as bypass_cache and auto_inject_escapes, but it does not explain parameters directly or exceed what the schema already conveys.

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 ('executes') and resource ('JevGuard evaluation pipeline'), then names four concrete pipeline stages. This clearly distinguishes it from sibling sub-tools such as jevguard_prune_state and jevguard_calibrate.

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 'pipeline including state pruning, ... certainty calibration, and 0-token caching' implies this is the aggregate evaluation tool, while siblings are individual stages. It gives clear context for selection, though it stops short of explicitly naming alternatives or stating when not to use it.

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 are provided, so the description carries the full burden. It discloses several behaviors: removing nulls, empty collections, collapsing whitespace, and protecting against cyclic references. However, it doesn't disclose whether the operation mutates the input or returns a new object, whether it's destructive, or any side effects. The 'protecting against cyclic references' is a useful behavioral detail, but the mutation/return behavior is a significant gap.

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 sentence that packs in the core operations and the cyclic reference protection. It's concise and front-loaded with the main purpose. It could be slightly more structured (e.g., separating the pruning operations from the safety feature), but it's 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?

For a tool with 2 parameters, 100% schema coverage, and no output schema, the description covers the main purpose and key behaviors. However, it doesn't explain the return value (sanitized state? success indicator?), which is important for an agent to know what to do with the result. It also doesn't clarify whether the input is mutated in place, which is a meaningful gap for a tool that 'prunes' state.

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. The description adds context about what 'sanitize' means (removing nulls, empty collections, collapsing whitespace) which helps understand the 'state' parameter's purpose. The 'prune_lists' parameter is not explicitly mentioned in the description, but the schema covers it. Baseline 3 is appropriate since 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 clearly states the tool's function: sanitizing and pruning JSON state payloads, with specific operations (removing nulls, empty collections, collapsing whitespace, protecting against cyclic references). It distinguishes itself from sibling tools like jevguard_cache_fingerprint and evaluate_* tools, which have different purposes. However, it doesn't explicitly name a sibling alternative for comparison, so it doesn't fully differentiate within a family of similar 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?

The description implies when to use this tool: when you need to sanitize/prune JSON state payloads. It doesn't explicitly state when not to use it or mention alternatives. The sibling tools are clearly different (evaluation, patching, fingerprinting), so the context is somewhat clear, but there's no explicit guidance on when to choose this over another tool.

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 does convey that the tool is evaluative and configurable, which implies a non-mutating verification rather than a system change. However, it does not disclose whether the check performs network calls, writes cache state, or returns a simple verdict versus a detailed report, leaving a meaningful transparency gap.

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 sentence front-loads the operative verb, the object under evaluation, the three risk categories, and the configurable risk tolerance. There is no filler, redundancy, or burying of key information.

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 and full schema are enough for an agent to provide the required inputs and understand the tool's evaluation scope. However, there is no output schema and no statement of return behavior, so it is unclear whether the tool returns a boolean verdict, a list of findings, or a full report; operational details such as side effects are also absent.

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 already documents all five parameters with 100% coverage, so the description need not compensate for missing schema details. It adds contextual color about the risk categories being assessed and the risk-tolerance modes, but it does not provide per-parameter meaning beyond what the schema already offers.

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 ('Evaluates whether...') and a specific resource ('a code diff or patch'), and enumerates concrete risk categories: security regressions, broken syntax, and critical system impact. This clearly distinguishes it from sibling tools such as evaluate_command_safety, which target command safety rather than patch content.

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 intended use is implied: call this when a code diff or patch needs pre-merge verification under a chosen risk tolerance. However, the description never explicitly contrasts this tool with evaluate_command_safety or the other siblings, nor does it state when not to use it, so an agent must infer the selection from the name and schema.

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