Validate Indian GSTINs locally (Verhoeff), extract PAN, identify issuing state.
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Average 4.4/5 across 3 of 3 tools scored. Lowest: 3.8/5.
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.
extract_panAInspectExtract the 10-character PAN embedded in a GSTIN (positions 3-12, 1-indexed). Throws if the GSTIN is the wrong length or the embedded PAN is malformed. Does NOT verify the check character — use validate_gstin for that.
| Name | Required | Description | Default |
|---|---|---|---|
| gstin | Yes | 15-char GSTIN. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it throws on wrong length or malformed PAN, and that it does not verify the check character. No annotations provided, but description fully covers behavioral aspects.
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?
Two sentences, front-loaded with main action, no redundant information. Every sentence adds value.
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 low complexity (1 param, no output schema), description covers input constraints, edge cases, and relationship to sibling tools comprehensively.
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% but parameter description is minimal. Description adds meaningful context about extraction positions and error conditions, exceeding baseline of 3.
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?
Description clearly states it extracts the 10-character PAN from a GSTIN (positions 3-12, 1-indexed). Distinguishes from siblings like validate_gstin by specifying what it does not do.
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?
Explicitly says 'Does NOT verify the check character — use validate_gstin for that', providing clear when-not-to-use and a specific alternative tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
state_from_gstinAInspectResolve the Indian state/UT name from a GSTIN's first 2 characters (the TIN state code). Accepts a full GSTIN or just the 2-char prefix. Returns {state_code, state_name}, e.g. ('27', 'Maharashtra').
| Name | Required | Description | Default |
|---|---|---|---|
| gstin | Yes | Full GSTIN or 2-char state-code prefix. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility. It outlines core behavior (accepts full GSTIN or prefix, returns state code and name) but omits error handling or edge cases (e.g., invalid prefix).
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 two sentences, front-loaded with the action, and includes an example output. Every sentence adds value without redundancy.
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 1-parameter lookup with no output schema, the description covers essential behavior (input, output format, example). It is mostly complete, though missing error or validation details.
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 single parameter 'gstin' has 100% schema coverage. The description reiterates the schema's text without adding new meaning, meriting the baseline score of 3.
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 the tool resolves Indian state/UT name from GSTIN's first 2 characters, with specific input and output examples. It is distinct from siblings like extract_pan and validate_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?
The description does not explicitly state when to use this tool versus alternatives. It implies usage for state resolution but lacks exclusion criteria or context-based guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_gstinAInspectValidate 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.
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