Invoice Table

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

Annotations already establish that the operation is read-only, idempotent, non-destructive, and closed-world. The description adds meaningful behavioral context beyond that: it enumerates the analysis steps, states that Invoice Table does not retain input or output after the response, and warns that human source review is required.

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 three sentences, front-loaded with the main action and followed by two important operational notes. Every sentence adds useful information, with no redundant restatement of the title or annotations.

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 that an output schema exists, the description need not explain return values. It covers the core processing behavior, data retention, and review requirement well. The main missing context is explicit sibling routing and parameter constraints, but overall it is sufficient to call the tool correctly.

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%, so the description must carry parameter meaning. It partially does by saying it processes supplied filenames and invoice text, which maps to the documents array. However, it does not mention the 1–25 item limit or that each document must contain both filename and text, so the schema still carries most of the structural burden without descriptive support.

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 states a specific verb and resource: processing supplied filenames and invoice text to extract fields, check dates and arithmetic, flag duplicates, and return derived records plus a review summary. It is clear what the tool does, but it does not explicitly differentiate itself from siblings like validate_invoice_records or export_invoice_records.

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 by the input description ('filenames and invoice text you supply') and the stated analysis outputs, but there is no explicit guidance on when to use this tool instead of validate_invoice_records or export_invoice_records. No exclusions or preconditions are given.

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?

Annotations cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely new behavioral facts: invalid and duplicate rows are preserved in the output rather than dropped, and the service retains neither input nor output after the response. That data-retention statement is the kind of thing an agent cannot infer from annotations. It stops short of describing row ordering or what the summary contains.

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, each carrying a distinct payload: what it does, what it returns, and two non-obvious behavioral guarantees. No repetition of the title or annotations, and the return values are 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?

An output schema and rich annotations mean the description need not explain return structure or safety, which it correctly does not. The remaining gap is the input contract: for a bulk record-ingesting tool with an undocumented nested schema and a 1000-record cap, an agent gets no guidance on what each record should contain or how malformed input is treated beyond 'remains visible.'

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% for the single `records` array, so the description carries the burden — but it only characterizes the input as 'reviewed invoice fields,' saying nothing about the per-record fields (category, filename), nullability, or the 1–1000 item bounds. It adds framing but not field-level meaning.

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 transformation plus return surface: processes supplied invoice fields and returns 'spreadsheet-safe CSV text, a filename, media type, and summary.' The qualifier 'reviewed invoice fields you supply' implicitly positions it downstream of validate_invoice_records, but no sibling is named, so differentiation is inferred rather than stated.

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?

'Reviewed invoice fields' implies the tool should be called after the records have been validated, which is useful sequencing context. However, there is no explicit statement of when to call this versus analyze_invoice_text or validate_invoice_records, and no exclusions or preconditions.

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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior. The description adds meaningful behavioral context beyond that: it discloses the data-retention policy ('does not store the tool input or output after the response') and states that returned records include derived validation and review fields.

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, front-loaded with the core operation and followed by the retention disclosure. No filler or repetition; each sentence contributes distinct 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?

The tool has one input parameter, rich annotations, and an output schema, so the description need not explain return shapes in detail. It covers what the tool does, the checks it performs, and a useful retention caveat. The remaining gap is lack of sibling-routing guidance, which is minor for this low-complexity tool.

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%, so the description must compensate for undocumented parameters. It says only that 'structured invoice fields you supply' are processed and lists the validation checks; it does not explain the records array, item fields, constraints, or expected formats. The schema itself defines field names, but the description adds little semantic clarity about the single required parameter.

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 states a clear verb ('Processes') and resource ('structured invoice fields') and enumerates the checks performed (missing values, dates, arithmetic, duplicates). It implicitly contrasts structured input with the text-based sibling, but does not explicitly name or distinguish analyze_invoice_text or export_invoice_records.

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 no explicit when-to-use guidance, no prerequisites, and no alternatives. An agent can infer that it validates structured invoice records, but there is no direction on when to choose it over analyze_invoice_text or export_invoice_records.

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