gstin_checkValidate an Indian GSTIN by structure (2-digit state code + PAN + entity code + 'Z') and its mod-36 cross-sum checksum.
| Name | Required | Description | Default |
|---|---|---|---|
| gstin | Yes | 15-character Indian Goods and Services Tax Identification Number. |
| 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 cover safety (readOnlyHint, idempotentHint), so the bar is lowered. The description adds the behavior that the check is algorithmic – structure and mod-36 cross-sum checksum – implying that this is a format-only validation rather than a database lookup. It does not explicitly disclaim that registered/active status is not verified, but the algorithm detail is sufficient context.
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, action-first sentence with no filler. It packs the two validation criteria compactly after the main verb-object, and every phrase earns its place.
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?
For a simple single-parameter check with an output schema and safety annotations, the description covers the mechanics of the check. The only gap is an explicit reminder that registration status is not verified, which is a minor caveat given the algorithm-based description.
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?
The schema already documents the single parameter with 100% coverage ('15-character Indian Goods and Services Tax Identification Number'), so the baseline is 3. The description enriches this by decomposing the GSTIN into state code + PAN + entity code + 'Z', giving agents a tangible sense of valid input composition.
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?
States the specific verb 'Validate' with a clearly delimited resource, 'Indian GSTIN', and names the exact validation criteria (structure and mod-36 checksum). This distinguishes the tool from the sibling family of check tools (iban_check, abn_check, etc.) because the scope is unambiguously India-specific GSTINs.
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 provides no explicit when-to-use or when-not-to-use guidance, nor does it name alternative tools. Usage context must be inferred entirely from the tool's name and the specific identifier type it targets, which is adequate for a family of one-format-per-tool check utilities but leaves routing entirely to the agent's common sense.
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.