data_quality_report

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

Annotations already indicate readOnly, idempotent, non-destructive. The description adds meaningful behavioral context by listing specific check types and stating it operates on one Odoo model. However, it does not explain potential resource impact or return format limitations.

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 front-loaded with the core verb and resource. It is concise, includes essential details, and has no wasted words.

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 5 parameters and no schema descriptions, the description provides high-level purpose but insufficient detail for correct invocation. The output schema exists but is not provided; however, the description does not explain return values. The tool's full usage context is incomplete.

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 parameter description coverage is 0%. The description mentions check types (duplicates, missing required, etc.) which likely correspond to the 'checks' parameter, but does not explain other parameters (instance, key_fields, sample_limit) or their formats. The agent must infer from parameter names.

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 runs read-only data-quality checks on one Odoo model and lists specific check types (duplicates, missing required values, orphaned references, format anomalies). This distinguishes it from sibling tools like search_records or aggregate_records.

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 use for data-quality inspection but does not provide explicit when-to-use or when-not-to-use guidance compared to alternatives like diagnose_odoo_call or inspect_model_relationships. Usage is inferred but not clarified.

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