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fa_validateMobile

Validate and normalize an Iranian mobile phone number, checking the operator prefix (MCI, Irancell, Rightel, Shatel) and the national format 09xxxxxxxxx. Use it to verify a phone number on a sign-up form or before sending an SMS. Returns validity, the normalized number and the operator. ($0.005 per call, USDC on Base)

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": {
      "value": "<value>"
    },
    "name": "fa_validateMobile"
  }
}

Input Schema

NameRequiredDescriptionDefault
valueYesmobile in any common format

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose key traits: it returns validity plus the normalized number and operator, and it surfaces the cost ($0.005 per call, USDC on Base). It does not say what happens on an invalid input (throws vs. validity=false) or any rate limits, so it is not fully complete.

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?

Three tightly packed sentences in a logical order: purpose/scope, when to use, then return values and cost. No filler sentences and every clause carries 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 validator with no output schema or annotations, the description covers purpose, usage, return shape, and cost. The only material gap is the behavior on invalid input (error versus falsy validity), which an agent would want before calling.

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 description coverage is 100% ('mobile in any common format'), so the parameter is already documented. The description adds the target normalization format (09xxxxxxxxx) and the operator set being checked, but that is mostly output-oriented context rather than new meaning for the input parameter, matching the baseline 3.

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?

States a specific verb and resource ('Validate and normalize an Iranian mobile phone number') and names the specific checks performed (operator prefix, 09xxxxxxxxx national format). The word 'mobile' also distinguishes it from the sibling fa_validateLandline without needing to name it.

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?

Gives concrete usage contexts ('on a sign-up form or before sending an SMS'), which is clear positive guidance. It stops short of naming alternatives or stating when NOT to use it (e.g., landline or non-Iranian numbers), so it falls just under the explicit when/when-not bar.

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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