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Ardaro Receipt Intelligence

Get a fixed free receipt example

get_receipt_example
Read-onlyIdempotent

Free, fixed synthetic receipt request and expected human-review result. No user document is processed and no payment is authorized. Use this to evaluate Ardaro before any paid call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitsYes
requestYes
synthetic_onlyYes
example_versionYes
expected_responseYesReceipt Intelligence v1 response. result is authoritative; receipt is a derived compatibility projection that MUST match result.
input_descriptionYes
paid_processing_performedYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond that by stating no user document is processed and no payment is authorized, and that the result is a fixed synthetic example. This reinforces the safety and determinism of the operation without contradicting annotations.

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 long, with the core purpose stated first and the usage guidance second. Every sentence earns its place, and there is no redundant or filler content. It is appropriately front-loaded and easy to parse.

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 zero parameters, a clear output schema, and annotations that fully cover the safety profile, the description provides sufficient context for an agent to invoke it correctly. It explains what the tool returns, what it does not do, and when to use it. Nothing essential is missing.

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?

The tool has zero parameters, so the baseline is 4. The description accurately implies the request is fixed and requires no input, which aligns with the empty input schema. It adds no unnecessary parameter details because none exist.

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 identifies a specific verb and resource: 'get' a 'fixed free receipt example'. It also distinguishes the tool from the sibling analyze_receipt by stating it is a synthetic example for evaluation, not a real analysis. The phrase 'expected human-review result' adds clarity about what is returned.

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 explicitly states when to use this tool: 'Use this to evaluate Ardaro before any paid call.' It clarifies that no user document is processed and no payment is authorized, which helps an agent decide this is a safe, non-production trial call. It does not explicitly name the sibling analyze_receipt as the alternative for real processing, so it misses a direct exclusion.

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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TDQS

A4.5/5.0
Disambiguation5/5

Only two tools exist, and their purposes are completely distinct: one performs a paid receipt analysis, the other provides a free fixed example. No agent could confuse them.

Naming Consistency5/5

Both tool names follow the same verb_noun snake_case pattern: analyze_receipt and get_receipt_example. The naming is predictable and consistent.

Tool Count3/5

With only two tools, the server is on the thin side for a general-purpose service, but for a narrowly scoped paid receipt analysis endpoint plus a free example, the count is borderline acceptable. Each tool has a clear role.

Completeness4/5

The core capability (analyze_receipt) is fully covered, and the example tool provides an evaluation path. Minor gaps exist (e.g., no batch analysis or status endpoint), but these are not obvious dead ends for the stated purpose.

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