isin_checkValidate an ISIN (ISO 6166) by structure and Luhn mod-10 check digit over the A=10…Z=35 converted string.
| Name | Required | Description | Default |
|---|---|---|---|
| isin | Yes | 12-character ISIN: 2-letter country code + 9 alphanumerics + check digit; spaces allowed. |
| Name | Required | Description | Default |
|---|---|---|---|
| tool | No | ||
| scope | No | ||
| valid | Yes | ||
| reason | Yes | ||
| country | No | ||
| normalized | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only and idempotent, so the bar is lower. The description adds meaningful behavioral detail by specifying the structure check and the Luhn mod-10 computation with the A=10…Z=35 conversion, making the validation logic transparent beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that communicates the core functionality and algorithm without any filler. It is front-loaded with the primary action and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter, a detailed schema description, an output schema, and readOnly/idempotent annotations, the description fully covers what an agent needs to understand the tool's behavior. No missing context is critical for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the parameter description already details the 12-character format and allowed spaces. The description adds value by explaining the transformation to numeric values, which is relevant to the validation semantics and not present in the schema field description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Validate') with a well-defined resource ('ISIN (ISO 6166)') and explicitly names the validation method (structure plus Luhn mod-10 over the converted string). This clearly separates it from generic checkers like luhn_check and other instrument-specific validators among the siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly states the tool validates an ISIN, which gives an agent direct context for selecting it. It does not explicitly call out alternatives or exclusion cases, but the specialized resource and algorithm make the intended use unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.