Fakturai — German E-Invoice (ZUGFeRD / Factur-X / E-Rechnung)

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

No annotations provided, so description carries full burden. It discloses what is checked (health, status, version pins vs. latest) but does not explicitly state it is read-only or has no side effects. Adding 'read-only' would improve transparency.

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?

Two sentences: first defines purpose, second gives usage context. No redundant words, perfectly front-loaded. Every sentence earns its place.

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 output schema exists, return values need not be described. Tool is simple with no parameters, and usage context is sufficient. Could mention that it does not modify state, but overall complete.

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?

Zero parameters, so schema coverage is 100%. The description does not need to add parameter meaning. Baseline of 4 is appropriate.

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 checks e-rechnung engine health, specifically KoSIT + Mustang daemon status and dependency version pins. It distinguishes from sibling tools generate_einvoice and validate_einvoice by being a pre-check 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?

Explicitly advises use before bulk invoice generation to confirm compliance gate is operational. While it does not list alternatives or when not to use, the context is clear given siblings. A score of 4 reflects good guidance without exclusions.

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

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

With no annotations, the description discloses the output format and the important behavioral fact that every invoice has passed the KoSIT acceptance gate. It does not detail permissions, side effects, or rate limits, but covers the key behavioral promise.

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, well-structured paragraph with the main function, output, verification guarantee, and keywords. It is concise with no filler, though a clearer separation of sections could improve readability.

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?

Despite having an output schema (not shown), the description fails to explain the critical input parameters, leaving the agent uninformed about how to construct invoice_json and seller_profile. For a complex tool generating regulated documents, this is a significant gap.

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 0%, and the description adds no meaning to the two opaque object parameters (invoice_json, seller_profile). The agent has no guidance on required fields, structure, or examples for these inputs.

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 it generates a KoSIT-verified ZUGFeRD/Factur-X German e-invoice, specifies output fields (pdf_b64, invoice_number, validation_report), and distinguishes from siblings like validate_einvoice by emphasizing the generation and verification aspect.

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 usage for generating a verified German e-invoice, but does not explicitly state when to use this tool versus siblings like validate_einvoice or compliance_status. No when-not or alternative guidance is provided.

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

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

With no annotations, description fully discloses input formats and the three validation steps. No contradictions. Could mention output format, but output schema exists. Good transparency for a read-only validation tool.

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?

Four concise, front-loaded sentences. Each sentence adds unique value: purpose, input format, validation details, and authentication status. 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?

Given the tool's complexity (2 params, 0% schema coverage, no annotations), the description covers input, validation behavior, and authentication. Output schema exists so return values are not needed. Could mention error handling, but overall complete.

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 has 0% description coverage; description compensates by explaining the 'content' parameter accepts base64-encoded PDF or raw CII XML and mentions default format. Adds significant meaning beyond the schema's bare titles.

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?

Description clearly states the tool validates existing ZUGFeRD/Factur-X/E-Rechnung invoices, listing specific formats (PDF, CII XML) and validation checks (PDF/A, EN 16931, KoSIT). This distinguishes it from sibling tools like compliance_status and generate_einvoice.

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?

Explicitly states 'No authentication required' and implies usage for validating existing invoices. Does not explicitly contrast with siblings, but the verb 'validate' clearly differentiates from generation or status checks.

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