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Glama

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

A4.1/5.0
Behavior4/5

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

Annotations readOnlyHint=true and idempotentHint=true already tell the agent the call is read-only and idempotent. Beyond that, the description discloses the paid nature, the single-voucher/settlement flow, and the signed XDR-1 receipt for the entire batch, which is genuinely useful behavioral context not present in the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences carry the core capability, pricing, and billing behavior without fluff. The enumeration of 17 check types is a bit long but serves as a quick inventory for the agent; however, this list partially duplicates the schema enum and could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one parameter, a rich schema, and an output schema present, the description covers the essential operational details: max count, cost model, and per-batch receipt. It does not explain partial-failure behavior, but the output schema likely covers per-item results, so nothing critical seems missing.

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 has full description coverage for the only parameter, items, including the {tool, value} shape and max/min bounds. The description enumerates the acceptable check types, but these are already in the schema enum, and it adds no new parametric meaning beyond what the schema provides, so the baseline 3 applies.

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 verb and resource: 'Run up to 50 identifier checks in ONE paid call,' so the agent immediately knows it is a batch execution tool. It also distinguishes itself from the many single-check sibling tools by emphasizing the batch scope and the one-payment model.

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 batch-vs-single tradeoff explicit with 'one voucher, one settlement' and 'at 50 items, 10x cheaper per check than single calls,' which tells an agent this is the cost-efficient route for multiple validations. It does not explicitly state when to choose a single-check sibling instead, but the context strongly implies that for one check you would not use a batch tool.

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

A3.9/5.0
Disambiguation5/5

Each tool targets a distinct identifier type or specific function (e.g., IBAN vs. EIN vs. block info), with no overlap even among similar validation routines. The batch and rental tools combine distinct sub-checks without ambiguity, making misselection unlikely.

Naming Consistency4/5

Tool names are uniformly snake_case and mostly follow a descriptive pattern, but the action verbs vary (e.g., 'check', 'format', 'info', 'guard', 'verdict') rather than a single verb_noun structure. While clearly readable, the pattern is not perfectly uniform.

Tool Count2/5

At 28 tools, the server feels like a broad utility pack rather than a focused domain. The count exceeds the 'heavy' threshold and includes three distinct sub-domains (financial identifiers, rental fraud, on-chain queries), making the surface area hard to navigate coherently.

Completeness4/5

The identifier validation coverage is thorough (most common checksums), rental checks cover risk, deposit, and verdict, and on-chain tools handle balances, activity, and settlement history. Minor gaps exist (e.g., no token transfer history, no generic identifier detection) but agents can likely work around them.

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