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state_from_pan

Validate a PAN (Permanent Account Number) and extract the entity type (individual / company / HUF / firm / trust / etc.) from the 4th character. NOTE: PAN's 4th character encodes entity type, not state — this tool surfaces the entity type and validates structure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
panYes

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description must disclose behavior. It explains validation and extraction from the 4th character, but does not specify error handling or output format. This is adequate but leaves some gaps.

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

Conciseness5/5

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

The description is two concise sentences: first sentence defines purpose, second sentence adds a crucial clarifying note. No redundant words, and the main action is front-loaded.

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?

Given the tool's simplicity (1 param, no output schema, no annotations), the description covers the core functionality and clarifies a common misconception. It doesn't specify the output format, but that is acceptable without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no description for 'pan' (0% coverage). The description compensates by explaining what PAN is and how the tool processes it (validate, extract entity type from 4th character), adding significant meaning beyond the schema.

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 clearly states the tool validates a PAN and extracts the entity type from the 4th character, listing examples (individual, company, HUF, firm, trust). It distinguishes itself from a common misinterpretation (extracting state), making its purpose unambiguous.

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 includes a clarifying note that the tool extracts entity type, not state, which helps prevent misuse. However, it does not explicitly state when to use this tool over siblings or when not to use it, but the siblings are not direct alternatives for PAN validation.

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

A3.7/5.0
Disambiguation4/5

The tools cover distinct tasks: script detection, identifier validation, address/name normalization, and transliteration. Some overlap exists (e.g., PIN codes appear in both pincode_to_locality and normalize_address), but descriptions clarify each tool's role.

Naming Consistency3/5

All names use snake_case, but the pattern varies: detect_script, normalize_address, and normalize_name are verb_noun, while gstin_info, pincode_to_locality, and state_from_pan are noun_verb. transliterate is just a verb, breaking the pattern.

Tool Count5/5

With 7 tools, the count is well within the ideal range (3-15) and appropriately covers the server's domain of Indian text and identifier processing without being excessive.

Completeness4/5

The toolset covers core functionality: script detection, address/name normalization, PIN and PAN/GSTIN extraction, and transliteration. Missing operations like Aadhaar validation or bulk processing are minor given the stated purpose.