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detect_script

Detect the dominant script of input text. Returns primary script + per-script character counts + a confidence score.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses return values (primary script, counts, confidence) but does not address edge cases like empty input, mixed scripts, or potential errors. Basic behavioral information is present.

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 a single, well-structured sentence that front-loads the action verb and resource. Every word is informative, and there is no extraneous text.

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 simplicity (1 parameter, no nested objects, no output schema), the description covers the essential purpose and return values. It misses details like confidence score range or behavior for empty strings, but overall it is sufficiently complete for a straightforward tool.

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

Parameters3/5

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

With 0% schema description coverage, the description must compensate. It states 'input text' for the sole parameter, which is implicit but functional. No additional constraints (e.g., encoding, length limits) are provided, so it adds minimal value 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 specifies the verb 'Detect' and the resource 'dominant script of input text'. It also lists return values (primary script, per-script counts, confidence score), making the tool's function distinct from sibling tools which deal with addresses, PAN, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide explicit guidance on when to use this tool versus alternatives. However, the sibling tools are unrelated, so usage is implied. No exclusions or non-usage scenarios are mentioned.

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.