validate_recipe

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

Since no annotations are provided, the description carries the full burden. It clearly states the behavior: returns a list of errors if invalid, and with auto_fix=true, automatically fills specific simple missing fields (id, author_id, version, previous_authors, variables) while leaving structural errors for manual correction. This gives concrete insight into what auto_fix does and does not do. It does not mention side effects like persistence, but the 'returns the repaired profile' phrasing implies no saving occurs.

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 two sentences, front-loaded with the main purpose. The second sentence efficiently explains the auto_fix behavior and its limits. No fluff or repetition; every clause adds meaningful information.

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 two parameters and nested recipe object, the description covers the essential behavior: validation outcome (errors list) and auto_fix's effect (repaired profile). There is no output schema, so describing return types is necessary and adequately done. Minor omission: it doesn't state what happens when validation succeeds without auto_fix (presumably an empty list), but this is a minor gap in an otherwise complete description.

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 coverage is 100% with descriptions for both parameters, so baseline is 3. The description adds value by enumerating exactly which fields auto_fix fills ('id, author_id, version, previous_authors, variables') and clarifying that structural errors are not auto-fixed. This goes beyond the schema's generic 'simple missing fields' description, providing actionable semantic detail.

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 'Validate a recipe JSON against the Meticulous profile schema' – a specific verb (validate) and resource (recipe JSON) that clearly defines the tool's purpose. It also distinguishes itself from the many sibling CRUD/profile tools by focusing on validation rather than retrieval or mutation.

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 when to use this tool: for validating a recipe against a schema. It makes clear that auto_fix handles only simple missing fields and that structural errors require manual correction, providing context for when to rely on auto_fix versus manual fixing. However, it does not explicitly mention alternatives or when not to use this tool, though the distinct validation role makes the usage context unambiguous.

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