Ready APIs

BIN / Card Lookup

bin_lookup

Read-onlyIdempotent

Identify the issuing bank, scheme (Visa / Mastercard / Amex / etc), card type (credit/debit/prepaid), and country behind a card BIN. Accepts a 6-8 digit BIN or a full PAN (Luhn-checked, never echoed). Returns scheme + card_type + brand + issuer_bank + country + risk_band. PCI-safe.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "card_or_bin": "<card_or_bin>"
    },
    "name": "bin_lookup"
  }
}

Input Schema

Name	Required	Description	Default
`card_or_bin`	Yes	6-8 digit BIN or full PAN (with or without spaces/dashes).
`use_binlist_fallback`	No	When false, force offline-only response (curated table + scheme prefix detection only).

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 14, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond the readOnly/idempotent/non-destructive annotations, the description discloses meaningful behavior: Luhn-checking, that full PANs are never echoed, PCI-safety, and the exact fields returned. This gives an agent a concrete safety and data-handling picture without needing to inspect external documentation.

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?

Three dense, front-loaded sentences with no wasted words. The first sentence establishes purpose and outputs, the second covers input validation and safety, and the third summarizes the return contract. Every sentence earns its place.

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?

With no output schema, the description compensates by explicitly listing return fields: scheme, card_type, brand, issuer_bank, country, and risk_band. It also covers input formats, validation, and PCI-safety. Combined with the schema and readOnly annotations, this is complete enough for an agent to invoke the tool correctly.

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%, so the baseline is 3. The description adds value by clarifying that input is validated via Luhn and that the PAN is never echoed, going beyond the schema's format description. The use_binlist_fallback parameter is already well-described in the schema, so no additional compensation is needed there.

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 ('Identify') and resource ('card BIN'), listing the exact data points returned: issuing bank, scheme, card type, and country. It is clearly distinct from any sibling tool, as nothing else in the sibling list covers BIN/card lookup. The input and output scope are unambiguous.

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 makes the use case clear: use this when you need to identify card/BIN issuer, scheme, type, country, and risk band. It does not explicitly name alternatives or state when not to use it, but the context is strong enough that an agent can infer the appropriate invocation.

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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