explain_belief

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

No annotations provided, so description carries full burden. It explains what the tool returns (supports, assumptions, retraction consequences) but does not disclose if read-only, error conditions, or performance implications. 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?

Very concise: two sentences plus clear Args list. No extraneous information, well-structured, 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?

Given existence of output schema (not shown), description doesn't need to detail return format. It covers main behavior and parameters. Could mention error handling or prerequisites, but complete enough for a simple tool.

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 description provides all parameter meaning: 'node_id: The belief ID to explain' and 'project: Project name or UUID, uses default if empty.' This fully compensates for schema's lack of 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 'Explain why a belief is IN or OUT by tracing its justification chain,' specifying the verb (explain) and resource (belief justification). It distinguishes from siblings like get_belief (retrieval) and what_if (hypotheticals).

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 context: understanding belief justification, but does not explicitly state when not to use or compare with alternatives like ask or search. The sibling list provides some context.

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