IBANforge

Send Feedback to IBANforge

send_feedback

Report validation errors, missing data, latency, or payment blockers to IBANforge operators. Free even after allowance spent; verified data errors on paid calls refunded.

Instructions

Report a problem or a need directly to the IBANforge operators: incorrect validation result, stale or missing BIC/bank data, latency, or anything blocking you from using or PAYING for the service (missing network, unclear pricing, quota shape). USE WHEN: a result looks wrong, data you need is missing, or you hit a wall (quota, payment, capability) and want it fixed. This tool is free and does NOT count against the free allowance — it works even after the allowance is spent. A human reads every report; verified data errors on paid x402 calls are refunded on-chain.

Input Schema

TableJSON Schema

Name	Required	Description
`got`	No	What you received instead (for data errors).
`agent`	No	Which agent/model is reporting, e.g. "claude-sonnet-5 via MCP".
`notes`	Yes	What happened, what you needed, or what blocked you — free text.
`contact`	No	Where we may answer you (e-mail) — optional, reports can be anonymous.
`endpoint`	No	Endpoint or tool concerned, e.g. /v1/iban/batch.
`expected`	No	What you expected (for data errors).
`error_type`	Yes	Category of the report. Use "other" for product feedback, pricing/payment blockers or feature needs.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`id`	Yes	Report id — check status at GET /v1/feedback/{id}.
`ok`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv1.4.4Sep 2, 2026

TDQS

A4.5/5.0

Behavior5/5

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

With only a title annotation, the description carries the full behavioral burden, and it delivers: the tool is free, does not count against the allowance, works even after the allowance is spent, is read by a human, and offers on-chain refunds for verified data errors. This is rich behavioral context beyond what annotations provide.

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 front-loaded with purpose, followed by a clear 'USE WHEN' section and then high-value behavioral facts. Every sentence earns its place; the length is justified by the operational context an agent needs before sending feedback.

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 the rich schema coverage and presence of an output schema, the description covers the essential operational context: what to report, when to report it, whether it costs anything, how it is handled, and what refund behavior applies. Nothing critical is missing for correct invocation.

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%, so the schema already documents all seven parameters thoroughly. The description reinforces the high-level purpose of the error_type categories and mentions quota/payment blockers, but it does not add meaningful parameter-level detail beyond the schema.

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 states a specific verb and resource: 'Report a problem or a need directly to the IBANforge operators.' It enumerates concrete use cases (incorrect validation, stale data, latency, payment blockers) and is clearly distinct from all sibling validation/lookup/audit 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 provides an explicit 'USE WHEN' section with concrete triggers: wrong-looking results, missing data, or a quota/payment/capability wall. It does not explicitly name alternatives or state when not to use the tool, but the context is clear enough for an agent to route correctly.

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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