what_if

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

No annotations provided, so the description carries full burden. It states the tool is a simulation ('without modifying the database') and describes the output (cascade of beliefs). This gives good insight into behavior, though it could mention permissions or error conditions.

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 no wasted words: a header line explaining the simulation, a paragraph on what it shows, and a clear Args list. It is well-structured and front-loaded.

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 the output schema exists but its content is not provided, the description explains the output (cascade of beliefs) adequately. It covers purpose, parameters, and behavior, making it complete for the tool's functionality.

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 adds full meaning for all three parameters in the Args section: node_id (the belief ID), action ('retract' or 'assert'), and project (name or UUID, default if empty). This compensates completely for the missing schema 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 simulates retracting or asserting a belief without modifying the database, and explains what it shows (cascade of beliefs going out or coming in). This distinguishes it from sibling tools like get_belief and list_beliefs which are read-only 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 says 'Use this to understand the impact of changing a belief', providing clear usage context. It does not explicitly mention when not to use or list alternatives, but the guidance is sufficient for correct invocation.

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