validate_invoice

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions validation actions but lacks details on permissions, rate limits, error handling, or output format. For a validation tool with zero annotation coverage, this is insufficient, as it doesn't describe what happens during or after validation beyond the basic purpose.

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 concise and front-loaded, stating the core purpose in the first sentence and adding a brief English translation. Both sentences earn their place by clarifying the tool's function, though it could be slightly more structured to highlight key aspects like validation levels. No wasted words, but minor improvements in organization are possible.

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 tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks context on usage, behavioral traits, and output expectations. With 100% schema coverage, it compensates partially, but for a validation tool without annotations or output schema, more completeness is needed to guide an AI agent effectively.

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 schema already documents all parameters (xml, base64, level) with descriptions and enum values. The description adds no additional parameter semantics beyond what the schema provides, such as explaining trade-offs between 'xml' and 'base64' inputs or elaborating on 'level' choices. Baseline 3 is appropriate as the schema does the heavy lifting.

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's purpose: validating e-invoice XML against XRechnung/ZUGFeRD standards with syntax, mandatory fields, and German business rules. It uses specific verbs ('prüft', 'validates') and identifies the resource (XML). However, it doesn't explicitly differentiate from sibling tools like 'get_format_info' or 'extract_data', which might also involve XML inspection.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'create_xrechnung' or 'convert_format', nor does it specify prerequisites or contexts for validation. Usage is implied but not explicitly stated, leaving gaps for an AI agent to determine appropriateness.

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