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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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
card_or_binYes6-8 digit BIN or full PAN (with or without spaces/dashes).
use_binlist_fallbackNoWhen false, force offline-only response (curated table + scheme prefix detection only).

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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TDQS

B3.2/5.0
Disambiguation2/5

Multiple tools have genuinely blurry boundaries: company_change vs company_changes differ only by singular/plural yet serve different purposes, company_domain vs company_classify vs company_lookup_auto all accept a domain, geo_zip_lookup vs geo_enrich vs geo_zip_batch all return ZIP profiles, and email_validate subsumes much of email_disposable and email_free_provider. The domain prefixes help narrow search space, but within many domains an agent cannot reliably predict which tool is the right one.

Naming Consistency4/5

All 129 tools uniformly follow a snake_case [domain]_[topic] convention (company_, fx_, geo_, dns_, weather_, tax_), which is highly predictable and consistent. Minor deviations include the confusing company_change/company_changes pair, and inconsistent suffix usage (_batch appears on address_validate_batch, company_domains_batch, geo_zip_batch but not on equivalent lookup tools elsewhere).

Tool Count1/5

129 tools far exceeds the 50+ extreem-mismatch threshold, bundling roughly 28 unrelated data domains (weather, fx, tax, ccompany, dns, jobs, flight, email, phone, tax...) into a single MCP surface. Even focusing on one domain forces the agent to load an enormous unrelated tool list; this should be split into many smaller domain-specific servers.

Completeness4/5

Per-domain coverage is impressively thorough: weather spans current/forecast/hourly/historical/normals/marine/route/air-quality, fx covers rates/convert/historical/volatility/correlation/strenth, and company includes lookup/enrichment/networks/timeline/peer-comparison plus six buyer-tuned signals with profile-introspection tools. Minor gaps like flight being historical-only and smtp probes skipping major email providers are documented scope decisions rather than dead ends.

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