Receipt checks for expense and bookkeeping agents: normalize receipt text or structured extraction and review arithmetic, confidence and duplicate indicators. Free fixed synthetic example. Caller-supplied analysis: 0.25 USDC on Base via owner-approved x402. No image OCR or automatic bookkeeping.
Extract and validate structured expense data from receipt text or a provider-neutral receipt extraction. Use when an agent needs merchant, date, tax, total, line items, confidence, duplicate detection, and arithmetic validation before creating or reconciling an expense. The result is advisory and always requires human review. Paid tool: 0.25 USDC per accepted request. Requires an owner-authorized x402-capable client. Use get_receipt_example for a fixed free example.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | ||
| contract_version | Yes | ||
| existing_fingerprints | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, so the description's burden is lighter. It adds valuable behavioral context not captured by annotations: the result is advisory and requires human review, the tool costs 0.25 USDC per accepted request, and it requires an owner-authorized x402-capable client. No contradiction with annotations exists.
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 sentences, each earning its place: the core function, the intended use case, the advisory/cost/auth constraints, and a pointer to the free sibling tool. The most important information is front-loaded, and there is no filler or repetition of schema details.
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 does not need to explain return values. It covers when to use the tool, prerequisites, cost, human-review requirements, and the alternative tool. For a read-only analysis tool with rich schema and annotations, the description is complete enough for an agent to decide and invoke 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 compensate. It does convey that `input` can be receipt text or a provider-neutral structured extraction, and 'duplicate detection' hints at the role of `existing_fingerprints`. However, it does not explain the `contract_version` parameter or the SHA-256 fingerprint format, leaving some required parameters only implied.
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 uses a specific verb and resource: 'Extract and validate structured expense data from receipt text or a provider-neutral receipt extraction.' It lists the domain (expense data), the processing (extraction, validation, duplicate detection, arithmetic checks), and distinguishes itself from the sibling by directing users to get_receipt_example for a fixed free example.
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 explicitly states when to use the tool: 'Use when an agent needs merchant, date, tax, total, line items, confidence, duplicate detection, and arithmetic validation before creating or reconciling an expense.' It also specifies prerequisites (owner-authorized x402-capable client), cost, and names the alternative get_receipt_example for a free example.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| limits | Yes | |
| request | Yes | |
| synthetic_only | Yes | |
| example_version | Yes | |
| expected_response | Yes | Receipt Intelligence v1 response. result is authoritative; receipt is a derived compatibility projection that MUST match result. |
| input_description | Yes | |
| paid_processing_performed | Yes |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Add one secure layer between your agents and this server.
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.
Both tool names follow the same verb_noun snake_case pattern: analyze_receipt and get_receipt_example. The naming is predictable and consistent.
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.
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.