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Check a top-up

top_up_status
Read-only

Check whether a top-up was paid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topup_idYestopup_id from top_up.
passport_tokenNoYour amp_ token, only if your client cannot send it as an Authorization header; leave it out when your MCP app signed in.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • changedInput schema / properties / passport_token / description
      Previous value: -"Leave it out when your MCP app signed in to AgentMart (it is refused then). Otherwise your passport bearer token (amp_...) or session token (amp_s_...), only if your client cannot send it as an Authorization header. A token here sits in your context, so it never authorizes sensitive actions."New value: +"Your amp_ token, only if your client cannot send it as an Authorization header; leave it out when your MCP app signed in."
    • addedInput schema / properties / topup_id / description
      Added value: +"topup_id from top_up."
  2. First observed

TDQS

C2.8/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered structurally. The description adds almost nothing beyond that — it does not say whether payment status is eventually consistent, whether polling is expected, or how the result is expressed, which are the genuinely useful behavioral facts here.

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

Conciseness3/5

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

It is a single six-word sentence with no waste, so it is not verbose. However, the brevity reflects under-specification rather than disciplined economy — the sentence is too terse for a tool whose result semantics matter.

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

Completeness2/5

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 is the only place to explain what 'check' returns — a boolean, a status enum, or an order object — and it does not. Given the absence of an output schema, this leaves a real gap for an agent reasoning about the result.

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 description coverage is 100%: topup_id is documented as coming from top_up and passport_token explains the auth-header fallback. The description adds no parameter detail, but the baseline of 3 is appropriate when the schema already carries the semantics.

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

Purpose4/5

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

The description gives a specific verb and resource ('Check whether a top-up was paid'), which is clearer than the bare name/title. It does not, however, distinguish itself from adjacent siblings such as top_up, get_order, or claim_refund, so an agent must infer the relationship.

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

Usage Guidelines2/5

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

There is no guidance on when to call this versus top_up (create) or claim_refund, nor any mention of the natural flow (create a top-up, then poll this for its payment state). The agent is left to infer the entire usage context.

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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