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

Annotations already set readOnlyHint=false, openWorldHint=true, destructiveHint=false. The description adds valuable detail: it persists a record (so not read-only in the strict sense) but does not modify the sample, and it is billed. This clarifies the exact side effects beyond the annotations. There is no contradiction; the description enriches the behavioral context.

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 informative but a bit dense with several clauses and a doc link. It front-loads the purpose and then covers side effects, usage, and output. While not minimal, every sentence contributes value—no wasted words. It earns a 4 for being structured and efficient without being overly terse.

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 the complexity (3 parameters, nested objects, output schema exists), the description covers prerequisites, side effects, usage context, and output semantics. It even points to a documentation resource for interpretation. The output schema is present, so return values are structured. Nothing critical is missing for an agent to correctly invoke and understand the 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 100%, so the baseline is 3. The description does not add per-parameter detail beyond what the schema provides. It mentions 'protected_fields' indirectly via 'Leaf names you own,' but that is also in the schema. It does not explain the 'model' parameter or the exact structure of 'sample_json' beyond the schema. Thus it meets the baseline but adds no extra semantic value.

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 opens with a specific verb-resource pair: 'Analyze sample property ambiguity and relationship identity scoping before schema generation.' This clearly distinguishes it from siblings like analyze_schema and generate_sample. It also states what it produces (findings with competing interpretations and suggested_names) and the scoping output, making the tool's purpose unmistakable.

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?

It explicitly states when to use the tool: 'before schema generation' and 'Apply only approved corrections before create_schema_from_sample.' It also notes a prerequisite ('Requires editor') and a side effect ('billed analysis persists a record but does not modify the sample'). It references an alternative: 'Optional: generate_sample already checks its initial sample,' which helps the agent decide whether this tool is needed. This is thorough guidance.

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