normalize_name
Normalize an Indian personal name: strip honorifics, title-case, return canonical form and common spelling variants ('Sri'/'Shri'/'Sree'/'Shree').
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
Indic Normalize
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: stripping honorifics, title-casing, and returning variants. It implies a read-only operation, though does not specify side effects or response structure.
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?
Single sentence of 22 words, front-loads the main purpose, no redundant information.
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?
No output schema, but the description mentions returning 'canonical form and common spelling variants', giving some idea of the output. Could specify if multiple names are handled.
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?
Schema coverage is 0%, but the description clarifies that 'name' refers to an 'Indian personal name'. Does not add format or constraints, but provides minimal context 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 'normalize an Indian personal name' with specific actions (strip honorifics, title-case, return canonical form and variants), distinguishing it from sibling tools that handle addresses, pincodes, 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 explicitly state when to use this tool versus alternatives, but the sibling tools suggest it's for personal names only. Lacks explicit when-not-to-use or prerequisites.
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.