boolean.augment_compare

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

No annotations exist, so the description must fully disclose behavior. It mentions adding edges and re-simulating but does not clarify if the operation is destructive, requires specific permissions, or how simulation state is managed. The return values are listed but not explained.

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 of 20 words, efficiently front-loading the main action and outcomes. It avoids redundancy but could be rephrased for better readability.

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?

With 4 parameters, no output schema, and technical complexity, the description is incomplete. It mentions two return values without format or interpretation, and important aspects like error handling, edge cases, or performance are 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?

Schema description coverage is 0%, so the description should explain parameters. It implicitly references llm_edges and baseline_signature but does not clarify session_id or permuted_null. The parameter role is partially inferred but not explicitly stated.

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 the tool adds LLM edges, re-simulates, and gates values, with specific return metrics. It uses clear verbs and identifies resources (edges, simulation). However, jargon like 'gate llm_adds_value' and 'attractor_distance' may not be universally understood, slightly reducing clarity.

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 sibling tools (e.g., boolean.build_and_test, integration.convergence_check). It lacks context about prerequisites, alternatives, or scenarios where 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.