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Get a NumeroClaw order

get_order
Read-onlyIdempotent

Returns the status of a NumeroClaw reading order. Use it when the person asks whether their order is paid or their reading is ready. Inputs: orderId and orderToken, both returned when the order is created. Returns the status (awaiting_payment, expired, paid, queued, processing, completed or failed), the amount, the payment link while the order is unpaid, how long it usually takes while it is being written, and a short message for the person. The reading itself is sent only by email, to the address given with the order. A reading that is still being written has not failed: it is usually ready within a few hours and always within 6 hours. A failed status means the reading could not be produced and the payment will be refunded after a person at NumeroClaw reviews it.

Input Schema

NameRequiredDescriptionDefault
orderIdYesThe order id returned when the order was created.
orderTokenYesThe private order token returned when the order was created.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by enumerating every possible status value, explaining that delivery happens only by email, and setting a timing expectation (usually a few hours, always within 6). It also clarifies that a 'failed' status implies a refund after human review, which prevents the agent from misreporting an outcome.

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

Conciseness4/5

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

Front-loads purpose and usage before enumerating statuses and edge cases, and every sentence carries operational value. It is somewhat long and slightly repetitive about the writing-time expectation, but nothing is padding.

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?

There is no output schema, so the description fully carries the burden of describing return values — statuses, amount, payment link, timing, and message — and it also covers the failure/refund path. Nothing an agent needs in order to call and interpret this tool is missing.

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

Parameters3/5

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

Schema coverage is 100% and both parameters are already documented as being returned at order creation, so the description's 'both returned when the order is created' mostly restates the schema. The added hint that orderToken is a private credential is useful but marginal, so baseline 3 applies.

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?

States a specific verb and resource — 'Returns the status of a NumeroClaw reading order' — which is immediately distinguishable from the sibling create_order and list_products. The agent knows exactly what this tool retrieves 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.

Usage Guidelines4/5

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

Gives a clear trigger condition: 'Use it when the person asks whether their order is paid or their reading is ready.' It does not explicitly name create_order or list_products as alternatives or state when not to use it, but the trigger is unambiguous enough to route correctly in practice.

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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