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ibanchecker-mcp

by io.github.koraykoylu

Extract IBANs From Text

extract_ibans_from_text
Read-onlyIdempotent

Scan a free-form block of text and pull out every candidate IBAN, then validate each one.

Useful for unstructured sources such as emails, invoices, PDFs pasted as text, or chat messages where IBANs appear inline and may be split by spaces or surrounded by other words. Returns a JSON array of the IBANs found, each with its validation result (valid, countryCode, bank details when known); text containing no IBAN returns an empty list rather than an error.

Use this as the first step when the account number is buried in prose; pass the extracted IBANs to validate_bulk_ibans only if you need to re-check them separately. Input text is processed in memory and not stored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesArbitrary text to scan for IBANs, e.g. the body of an email or invoice. IBANs may be split across spaces or embedded in sentences.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already provide idempotentHint, readOnlyHint, and destructiveHint. The description adds valuable behavioral info: input is not stored, empty list returned for no IBANs, and processing is in memory. No contradictions.

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 concise, with each sentence earning its place. Well-structured: first functionality, then usage, then behavior. No wasted text.

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 simple tool with one parameter and no output schema, the description sufficiently explains the output format (JSON array with validation results) and behavior for empty input. Annotations cover safety.

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?

Schema coverage is 100% with a good description already. The description adds nuance about IBANs possibly being split across spaces, which enhances understanding 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 title and description clearly state that the tool scans text for IBANs and validates them. The verb 'extract' and resource 'IBANs from text' are specific, and it distinguishes from sibling tools like validate_iban.

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

Usage Guidelines5/5

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

The description explicitly says when to use (unstructured sources like emails, invoices) and when not to (after extraction, use validate_bulk_ibans only if re-check needed). It provides clear context and alternatives.

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

A4.8/5.0
Disambiguation5/5

Each tool targets a distinct operation: extracting IBANs from text, retrieving format specs, looking up BICs, and validating single or multiple IBANs. There is no overlap in functionality, so an agent can clearly distinguish which tool to use for a given task.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case (e.g., extract_ibans_from_text, validate_iban). The naming is predictable and aligns with common conventions, making it easy for an agent to interpret their purposes.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose. Each tool addresses a necessary function for IBAN checking without being too few or too many, providing a focused and manageable set.

Completeness5/5

The tools cover the full lifecycle of IBAN handling: extraction from text, validation of single and bulk IBANs, format lookup, and BIC enrichment. There are no obvious gaps, as the validation results already include bank details when available.

Resources