InvoiceForge

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

With no annotations provided, the description carries the behavioral burden and does well: it discloses that the operation is a 'Light read' and quantifies the cost at 0.2 credit, which is real operational context beyond the name. It stops short of covering response shape, caching, or rate limits, but the read-nature and cost are the essentials.

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?

A single dense sentence that front-loads what is returned, then closes with the cost note. Every clause carries information, including the useful enumeration of deliberate non-goals such as Peppol transmission and French accredited-platform duties.

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?

With no output schema, no annotations, and no parameters, the description must convey what the agent gets back, and it does: rules checked, syntaxes read/produced, and explicit exclusions. Nothing an agent needs to decide whether to call it is missing.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline of 4 applies. The description correctly spends no words on arguments.

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?

States a specific verb and resource: it returns the EN 16931 rules checked, the syntaxes supported, and the deliberate non-goals. This is clearly distinguishable from sibling action tools like validate_invoice and generate_invoice, so an agent can identify it as a capability-discovery read without opening the schema.

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?

Usage is implied rather than stated: the description positions it as a read-only capability lookup, which an agent can infer is useful before validating or generating. It never explicitly says when to call this versus, say, get_usage, nor does it name any exclusion criteria.

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 carries the full burden and does well: it discloses that missing fields are reported rather than guessed (a non-obvious behavioral trait), that output is structured JSON, and that each call costs 1 credit. It omits auth/permission requirements and error behavior, keeping it from a 5.

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?

Three tight sentences: what it does, when to use it, what it costs. No filler, and the core capability is front-loaded.

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?

For a single-parameter extraction tool with no output schema and no annotations, the description covers input formats, return semantics, missing-field behavior, and cost. Nothing essential for correct invocation is absent.

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?

Only one parameter exists and schema coverage is 100%, so the schema already documents it as 'The invoice XML, UBL 2.1 or CII'. The description echoes the accepted formats but adds no syntax or size guidance beyond the schema, so the baseline 3 applies.

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?

States a specific verb (reads/extracts) and resource (UBL or CII Factur-X invoice XML) plus the output form (structured JSON). It is immediately distinguishable from the generate_invoice and validate_invoice siblings, which produce or check rather than extract.

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 gives the usage context: 'Use it to ingest supplier invoices before booking them.' This tells the agent when the tool fits. It stops short of naming alternatives or exclusions (e.g., when to prefer validate_invoice), so it is clear but not fully routing.

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 carries the full burden and does well: it discloses that totals and the VAT breakdown are computed automatically, that the document is validated before being returned, and the cost (1 credit). The main gap is what happens on validation failure (error shape, whether partial data is returned).

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?

Three sentences, zero filler, front-loaded with what the tool produces, then the choice, then the guarantee. Even the credit-cost sentence earns its place as an operational constraint.

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?

No output schema exists, so the description usefully states the return artifact (validated UBL 2.1 XML) and the profile stamp. Complex nested input is fully covered by the schema. What remains thin is failure behavior and whether the produced XML is retrievable/stored after generation.

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, but the description adds value: it expands the terse enum values ('en16931', 'peppol') into their real specification names and explains the semantic model, and it frames the nested invoice payload as 'invoice data' that gets totals computed. It adds meaning beyond the raw enum.

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?

States a precise verb+resource+output artifact: turns invoice data into a compliant UBL 2.1 XML document following EN 16931. The profile choice (EN 16931 vs Peppol BIS Billing 3.0) lets an agent distinguish it from sibling validate_invoice and extract_invoice without opening schemas.

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?

Gives a real decision point for the profile parameter ('Choose the plain EN 16931 profile or the Peppol BIS Billing 3.0 customization'), which is usable guidance. However it never says when to reach for this tool instead of validate_invoice or extract_invoice, and gives no prerequisites or when-not-to-use conditions.

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 carries the full burden and does well: it discloses the cost profile ('Free to call') and the data window ('last 30 days'), which are genuine behavioral facts beyond the schema. It does not mention auth requirements or rate limits, so it falls short of a 5.

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 tight sentences with the contents front-loaded and the cost note appended. Every clause earns its place; nothing is repeated from the schema or title.

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?

There is no output schema, so the description must hint at the return shape, and it does by naming the three returned fields and their window. No annotations exist either, and the 'free to call' note covers the main behavioral concern. Minor gap: no mention of formatting or staleness.

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?

The tool takes zero parameters, which is the baseline-4 case. The description adds no parameter semantics because none exist, and needs none.

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?

States a specific verb (get) and resource (usage) and enumerates exactly what is returned: plan, remaining credit balance, and call volume over the last 30 days. It is clearly distinguishable from the invoice-oriented siblings (extract_invoice, generate_invoice, validate_invoice, describe_coverage).

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?

No explicit when-to-use or when-not-to-use guidance and no named alternatives, but for a zero-parameter, cost-free read tool the implied usage ('check your balance/usage') is reasonably self-evident. It provides no routing conditions.

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 carries the full burden and does well: it discloses tolerance of malformed input, the shape of the answer (each violation with rule identifier and business term, plus the set of rules checked), an explicit limitation ('never claims full compliance'), and a cost of 1 credit. Only idempotency/non-mutating nature and any size or timeout limits are left unstated.

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?

Three sentences, front-loaded with the purpose and then the behavioural contract. Nearly every clause earns its place, though 'the arithmetic that most often gets invoices rejected' is mild editorial colour rather than operational 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?

There is no output schema, so the description correctly compensates by describing the return payload (violations, rule ids, business terms, rules actually checked) and the scope limitation. For a one-parameter tool with deep nested invoice objects that is close to complete, missing only error/edge-case behaviour such as what happens with a structurally invalid payload.

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% and there is a single parameter, so the baseline is 3. The description adds meaning beyond the schema by stating that the invoice payload may be deliberately incomplete or malformed and that this is acceptable input rather than an error, so it goes slightly above baseline.

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?

States a specific verb (checks) and resource (invoice data) against a named standard (EN 16931 business rules) and enumerates the checked categories: mandatory fields, code lists, VAT category consistency and arithmetic. This clearly separates it from extract_invoice and generate_invoice, which do entirely different things with the same resource.

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 line 'Accepts incomplete or malformed data on purpose, since that is what you want checked' tells the agent the condition under which this tool is the right one, i.e. pre-validation of possibly-broken input. It does not explicitly name a sibling as an alternative or state when not to use it, so it falls short of a 5.

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