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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds minimal behavioral context by stating the output is a 'complete, runnable Python recipe', implying code generation. However, it doesn't elaborate on return format or side effects beyond annotations, which is acceptable given the safe nature.

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. It immediately communicates the key value (complete, runnable recipe) and the resource (common integration pattern), leaving the parameter details to the schema.

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 is simple (one parameter) and has an output schema, so the description doesn't need to explain return values. It sufficiently covers the core purpose, and the schema handles the pattern selection. It could slightly mention that the output is Python code, but 'recipe' already implies that, so completeness is high.

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% – the pattern parameter is fully documented with its four allowed values. The description does not add any parameter-specific meaning beyond the schema, so the baseline 3 applies.

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 provides 'Complete, runnable Python recipe' for a 'common AgentLedger integration pattern', specifying the verb and resource. It distinguishes itself from siblings like ledger_track or ledger_report by focusing on recipes/examples, which is a different purpose.

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 that it should be used for learning/starting points, nor does it exclude cases like when to use ledger_api_docs instead. The description is purely functional without context for selection.

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