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Batch validate (up to 50 checks, one payment)

batch_validate
Read-onlyIdempotent

Run up to 50 identifier checks (IBAN / LEI / VAT / UK company / SWIFT / ABA / EIN / IFSC / ABN / Luhn / ISBN / E.164 / SEDOL / ISIN / EAN-13 / GSTIN / context-distill) in ONE paid call: one voucher, one settlement, one signed XDR-1 receipt covering the whole batch. Flat price regardless of count — at 50 items, 10x cheaper per check than single calls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesList of {tool, value} checks (max 50).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNo
countYes
scopeNo
resultsYes
valid_countNo
invalid_countNo

TDQS

A4.3/5.0
Behavior4/5

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

The description adds operational detail beyond the annotations by disclosing 'one voucher, one settlement, one signed XDR-1 receipt' and flat pricing. This is useful context for an agent deciding whether to invoke the tool. There is no contradiction with the readOnlyHint, since payment/settlement is not described as mutating external data in the validation sense.

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 two dense sentences with no filler: the first presents the action, scope, and batching model; the second gives the pricing rationale. Key information is front-loaded and every clause 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?

Given the simple single-parameter schema, the existing output schema, and annotations, the description fully covers batching capability, payment behavior, and the economic reason to use it. An agent has enough to select and invoke the tool correctly without missing critical context.

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?

The input schema already provides 100% coverage for the single `items` parameter, including the `tool` enum and `value` descriptions. The description only restates the 'up to 50' limit and lists identifier types, adding no meaning beyond the schema, so the baseline of 3 is appropriate.

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 opens with a specific action and resource: 'Run up to 50 identifier checks... in ONE paid call.' It also differentiates itself from the many individual sibling validator tools by emphasizing batching and the per-check cost advantage over single calls.

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 clearly signals batch use cases through 'up to 50 checks in ONE paid call' and '10x cheaper per check than single calls,' which implies choosing this over individual sibling tools for multi-item validation. It stops short of an explicit 'use individual tools for a single check' exclusion, but the context is clear enough.

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

A3.8/5.0
Disambiguation5/5

Each tool targets a distinct identifier type or data source: every checksum validation is for a specific standard (IBAN, LEI, VAT, etc.), and the on-chain and rental tools are clearly separate domains. There is no ambiguity between tools; even similar-sounding checks like 'luhn_check' and 'ean13_check' are distinct algorithms with explicit descriptions.

Naming Consistency3/5

Most validation tools follow a 'X_check' pattern (iban_check, swift_bic_check, ein_format_check), but others deviate: 'company_number_format' uses a noun_noun structure, 'block_info' is noun_noun, and 'batch_validate' and 'context_distill' use verbs. While the pattern is not uniform, the names are readable and the convention is understandable, just not fully consistent.

Tool Count2/5

With 28 tools, this server exceeds the typical 3-15 well-scoped range and even the 16-25 heavy range. The server covers multiple unrelated domains (identifier validation, blockchain queries, rental fraud, text processing), which inflates the count. While each tool is individually justified, the overall surface feels overly broad for a single MCP server.

Completeness4/5

For each sub-domain, coverage is strong: identifier validation includes a wide range of international standards, on-chain queries cover balances, activity, and settlement history, and rental tools provide risk, verdict, and deposit guard. Minor gaps exist (e.g., no country-specific tax IDs beyond what's listed, no transaction history for arbitrary tokens), but the visible coverage is comprehensive for the stated purposes.

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