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Ingest a receipt (OCR pipeline)

receipt_ingest

Processes receipts through a multi-model pipeline, categorizes expenses, matches to jobs, and posts to the expense ledger.

Instructions

Run a receipt through the tiered extraction pipeline: fast model first, automatic escalation to a stronger model when confidence is low or the math does not reconcile. The result is categorized (media, fuel, equipment, safety gear, ...), optionally matched to a job for per-job P&L, duplicate-guarded, and posted straight to the expense ledger. Paper to books in one call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesRaw receipt text (from a photo OCR or typed)
jobIdNoJob to attribute this cost to
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: tiered extraction, automatic escalation to a stronger model, categorization, job matching, duplicate guarding, and posting to the expense ledger. This is comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the main action, and every phrase adds value. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and no annotations, the description provides a complete picture of what happens from input to output, including pipeline stages and side effects like duplicate guarding and ledger posting.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description adds context by explaining that 'text' is raw receipt text and 'jobId' is for job attribution, tying them to the pipeline behavior. It does not add entirely new semantics but reinforces usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Run a receipt through the tiered extraction pipeline... Paper to books in one call.' It uses specific verbs and resources, and the tool is distinct from all sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for processing receipts and mentions key behaviors like automatic escalation, but it does not explicitly state when not to use it or compare to alternatives. However, no sibling tool offers similar functionality, so context is clear.

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

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