Validate an Indian GSTIN (15-char Goods & Services Tax Identification Number). Checks length, character set, state code, embedded PAN format, and the GSTN mod-36 check character. Returns {valid, state, state_code, pan, entity_code, check_char, error?}.
| Name | Required | Description | Default |
|---|---|---|---|
| gstin | Yes | 15-char GSTIN, e.g. '27AAPFU0939F1ZV'. Case-insensitive; surrounding whitespace is trimmed. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It explicitly lists what is validated (length, character set, state code, PAN format, check character) and the return fields, which is comprehensive. However, it could mention error handling or edge cases.
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 concise with only two sentences, front-loading the purpose. Every sentence provides essential information without unnecessary verbosity.
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 and no output schema, the description fully explains the return object fields and validation checks. It is complete for an agent to invoke correctly.
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 description coverage is 100%, but the description adds value by stating the parameter is case-insensitive and that surrounding whitespace is trimmed, which goes beyond the schema. It also explains the expected format with an example.
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 clearly states 'Validate an Indian GSTIN' and lists specific validation checks (length, character set, state code, PAN format, check character), making the tool's purpose precise and distinct from siblings like extract_pan or state_from_gstin.
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?
While the description does not explicitly state when not to use the tool, the sibling context (extract_pan, state_from_gstin) implies this tool is for comprehensive validation, not for extracting sub-parts. The purpose is clear enough for an agent to decide.
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 has a clearly distinct purpose: extracting PAN, resolving state, and full validation. No overlap or ambiguity.
All tool names follow a consistent verb_noun pattern with snake_case (extract_pan, state_from_gstin, validate_gstin).
Three tools is slightly below typical range but well-scoped for a focused validation server; each tool is essential and not redundant.
Covers the main validation needs (format, state, PAN, check digit). Minor gaps like a dedicated check-digit tool or batch processing exist but are non-essential.