validate_against_schema

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

With no annotations, the description carries the full burden. It discloses key behavior: never raises exceptions, invalid schemas are reported via an 'error' key, and the return is a structured report with passed/failed/missing lists and human-readable reasons. This goes beyond a generic 'validates' and gives the agent a clear expectation of failure modes.

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 efficiently formatted with an Args and Returns section, front-loaded with the purpose sentence. It is not verbose—every line contributes to the agent's understanding. It could be tightened (the Returns section is slightly redundant with the behavioral note), but it's well-structured and clear.

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 two required parameters, no output schema, and no annotations, the description covers the essential context: what the tool does, what each parameter is, how errors are handled, and what the response contains. It references the sibling workflow (extract_fields) which aids routing. The only minor gap is that it doesn't specify the expected shape of 'data' beyond being an object, but the example mitigates this.

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 0%, so the description must explain the parameters. It does: 'data' is explicitly the output of extract_fields, and 'schema' is a JSON Schema. This adds meaning beyond the bare 'object' type in the schema and gives the agent the necessary context for both arguments. It could add more detail about expected structures, but it is sufficient for a two-parameter tool.

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 clear verb+resource: 'Validate extracted data against a JSON Schema.' It names the specific tool and its input, and the example referencing extract_fields ties it to the workflow. It is clearly distinct from the siblings (list_supported_document_types and extract_fields) because validation is an entirely separate operation.

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 gives clear context for when to use it: after extraction, with the 'data' from extract_fields. It implies the pipeline (extract → validate) without explicitly saying 'use this when...' but the example makes the usage obvious. It does not mention when not to use it or name alternatives, so it lacks an explicit exclusion but is not misleading.

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