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localecheck

localecheck

by qinisolabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

parse_address

Extract country, postcode, and city from free-text UK or US addresses for user onboarding, KYC checks, or address storage. Returns structured data with a confidence flag.

Instructions

Extract structured {country, postcode, city} from a free-text UK or US address. USE THIS when onboarding a user, running a KYC/fraud check, or storing an address, instead of splitting the string yourself. Returns a confidence flag.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input": "<input>"
    },
    "name": "parse_address"
  }
}

Input Schema

NameRequiredDescriptionDefault
inputYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. Mentions returns a confidence flag, but does not disclose behavior for non-UK/US addresses, error handling, or performance. Adequate but leaves room for improvement.

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?

Two sentences: first states purpose, second gives use cases and output. No wasted words, front-loaded with key 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?

For a single-parameter tool with no output schema and no annotations, the description covers purpose, use cases, and output (confidence flag). Missing details on confidence flag semantics and error behavior, but overall sufficient for an AI agent to use correctly.

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?

With 0% schema description coverage, the description adds critical meaning: 'free-text UK or US address'. This compensates well for the bare schema, though could specify input length or encoding.

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?

Clearly states the verb 'Extract' and resource 'structured {country, postcode, city} from a free-text UK or US address'. Distinguishes from siblings like parse_date, format_currency, etc., which are unrelated.

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?

Explicitly tells when to use: 'when onboarding a user, running a KYC/fraud check, or storing an address'. Advises against manual splitting. Although no explicit alternatives, siblings are unrelated, so guidance is clear.

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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