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

No annotations present, so description carries full burden. It mentions internal planning and session dedup via `known`, but does not disclose auth requirements, rate limits, error behavior, or side effects. Adequate but not thorough.

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 sentences that are information-dense but not overly verbose. Key information is front-loaded, though the second sentence could be more concise. Lacks bullet points or structure but still effective.

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?

Gives enough context for basic use but lacks details on output format, error handling, and behavior for many parameters given the tool's complexity (18 params, no output schema). Adequate but not fully comprehensive.

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 baseline is 3. The description adds value by explaining the `known` parameter's dedup role and the overall purpose of using the tool for reference knowledge, going beyond the schema's 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 clearly states the tool plans and returns content of load-eligible units for answering tasks. It links to kcp_plan but does not fully distinguish from other siblings like kcp_replay or kcp_trace, though the core purpose is specific.

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 some guidance on when to use (to answer a task) and how to treat the output (reference, not instructions). Lacks explicit when-not-to-use or comparison with alternative tools beyond mentioning kcp_plan.

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 full burden. It discloses that the tool is deterministic, inspectable, and performs no content loading or model invocation. This indicates a read-only planning behavior. However, it does not detail authentication, rate limits, or side effects, which are less critical for a planning 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?

Two sentences, zero waste. The first sentence packs key functionality, the second clarifies what it does not do. Front-loaded and efficient.

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 planning tool with no output schema and 17 parameters, the description covers core purpose, scope, and constraints. It lacks explanation of return format or error handling, but given the tool's nature, this is acceptable. Sibling context helps, but explicit return info would improve completeness.

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% and each parameter already has a clear description. The tool description adds high-level context (e.g., 'which units to load, order, skip decisions') but does not elaborate on individual parameters 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 clearly states it produces a deterministic load plan for a task against a KCP knowledge.yaml, listing what it covers (units, order, skip decisions, federation, budget) and explicitly what it does not do (no content loading, no model call). This differentiates it from sibling tools like kcp_load and kcp_trace.

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 that this tool is for planning before loading (e.g., 'no content is loaded and no model is called') but does not explicitly state when to use it versus alternatives or provide exclusions. An agent can infer usage context, but guidance is indirect.

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 present, so the description carries full burden. It describes the sequence of actions (re-fetch, compare, re-run planner) and the outcome (report identical or drifted). However, it does not disclose potential side effects (e.g., if any state is modified) or required permissions, which would enhance 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 concise (two sentences), front-loads the purpose, and uses efficient language. Every sentence adds value: the first explains the process, the second reinforces the tool's role.

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 simplicity (one parameter, no output schema, no nested objects), the description is complete. It explains the input, the entire workflow, and the expected output, leaving no gaps for the agent.

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 has 100% coverage with an explicit description for the single parameter 'artifact'. The tool description does not add additional semantic meaning beyond what the schema provides, so baseline score 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's purpose: cross-examining a saved plan artifact by re-fetching manifests, comparing sha256, and reporting drifts. It uses specific verbs and distinctly describes the action, distinguishing it from siblings like kcp_plan and kcp_validate.

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 usage after kcp_plan to verify its results ('plan is evidence; replay is cross-examination'). It provides clear context for when to use, though it does not explicitly mention when not to use or alternatives 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?

No annotations are provided, so the description must carry the full behavioral burden. It states it returns a plan plus verdicts but does not disclose side effects, authorization needs, or safety profile. Adequate but not thorough.

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 sentences, no wasted words. The first sentence immediately conveys the tool's purpose, and the second adds key points about input equivalence and output structure.

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 17 parameters, no output schema, and no annotations, the description gives the core idea but lacks depth on return format, edge cases, or behavioral nuances. Adequate but not fully comprehensive.

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 17 parameters. The description adds no additional meaning or context beyond summarizing that inputs are shared with kcp_plan. Baseline 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 produces a decision trace, annotates each manifest unit with gate cascade verdicts, and distinguishes it from the sibling kcp_plan by noting identical inputs but enhanced output.

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?

Mentions 'Same inputs as kcp_plan' and describes the extended output, which implies use when a trace is needed rather than a plain plan. Lacks explicit when-not or alternatives but provides sufficient context.

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 must cover all behavioral aspects. It only mentions what it checks (errors and warnings) but not whether it is read-only, what the output looks like, or any side effects. This is insufficient for a validation 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 concise with a single sentence that clearly states the purpose and scope. However, it could be more front-loaded with the primary action (validate) and include key details about warnings.

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 no output schema, the description should explain what the tool returns (e.g., success/failure, list of errors). It doesn't mention output format or how to interpret results, making it incomplete for an AI agent to use effectively.

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 single parameter 'manifest' is described as 'Path, directory, or HTTPS URL of a knowledge.yaml', adding useful context beyond the schema's property name. This helps the agent understand acceptable input formats.

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 it validates/lints a knowledge.yaml file for structural errors and navigation-weakening warnings. It distinguishes itself from siblings like kcp_load and kcp_plan by focusing on validation.

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?

No guidance on when to use this tool versus alternatives. It doesn't mention prerequisites or when to avoid using it, such as after modifications or for non-YAML files.

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