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Spam Check

vri_spam_check
Read-onlyIdempotent

Spam/scam/robocall reputation for one North American phone number, with an explainable risk block: the verdict, a confidence band, and the individual reasons behind it (complaint volume and recency, complaint category, community reports, line type, recent porting).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
phone_numberYesUS/Canada number, any common format

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
riskNoExplainable spam/risk verdict: WHY the number is flagged (or not), with an honest confidence band. A clean verdict means "nothing found against it", never "verified safe".
is_spamNoTrue if flagged spam/scam/robocall
spam_typeNoSCAM, ROBOCALL, or null when clean
phone_numberNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / risk
      Added value: +{
      +  "additionalProperties": true,
      +  "description": "Explainable spam/risk verdict: WHY the number is flagged (or not), with an honest confidence band. A clean verdict means \"nothing found against it\", never \"verified safe\".",
      +  "properties": {
      +    "confidence": {
      +      "description": "How much weight the evidence actually carries",
      +      "enum": [
      +        "low",
      +        "medium",
      +        "high"
      +      ],
      +      "type": "string"
      +    },
      +    "confidence_note": {
      +      "description": "One plain sentence explaining the band",
      +      "type": "string"
      +    },
      +    "reasons": {
      +      "description": "Individual pieces of evidence behind the verdict",
      +      "items": {
      +        "additionalProperties": true,
      +        "properties": {
      +          "code": {
      +            "description": "provider_flag, ftc_complaints, complaint_category, community_reports, line_type_voip, recently_ported, no_complaints",
      +            "type": "string"
      +          },
      +          "signal": {
      +            "enum": [
      +              "negative",
      +              "positive",
      +              "context"
      +            ],
      +            "type": "string"
      +          },
      +          "text": {
      +            "description": "Human-readable evidence",
      +            "type": "string"
      +          },
      +          "weight": {
      +            "enum": [
      +              "strong",
      +              "moderate",
      +              "weak"
      +            ],
      +            "type": "string"
      +          }
      +        },
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "verdict": {
      +      "description": "unknown = no reputation data was read for this number",
      +      "enum": [
      +        "scam",
      +        "robocall",
      +        "reported",
      +        "clean",
      +        "unknown"
      +      ],
      +      "type": "string"
      +    }
      +  },
      +  "type": "object"
      +}
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the tool read-only, idempotent, and non-destructive. The description goes further by disclosing the explainable risk block with verdict, confidence band, and reasons, plus the factors considered such as complaint volume, recency, community reports, line type, and porting. No contradiction with annotations.

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 dense sentence with no filler. It front-loads the core action and scope, then adds valuable output and behavioral detail. Every clause contributes useful 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?

Given the one parameter, read-only annotations, and mention of the output risk block, the description is nearly complete for an agent to invoke it. It could be slightly stronger with explicit guidance on when to choose this over vri_number_lookup, but it is sufficient for correct use.

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?

The input schema fully covers the single phone_number parameter with a description of 'US/Canada number, any common format.' The description adds little beyond 'North American,' so it mainly relies on the schema for parameter meaning.

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 identifies the tool as returning spam/scam/robocall reputation for a single North American phone number, including an explainable risk block. This distinguishes it from sibling tools like bulk lookup and general number lookup by emphasizing the single-number reputation focus.

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?

It states the tool is for one phone number, which implies it is not for bulk operations, and the scope is specific to spam reputation. However, it does not explicitly name alternative tools or provide when-not-to-use guidance relative to siblings like vri_number_lookup.

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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