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voucherifyio

voucherify-core-mcp

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by voucherifyio

get_campaign

Retrieve complete campaign details by ID, including discount settings, validation rules, and status for analysis and management.

Instructions

Retrieve detailed information about a specific campaign by its ID.

Returns complete campaign configuration including discount details, validation rules, voucher settings, and current status for campaign analysis and management.

Enhanced Features:

  • Automatically fetches and processes validation rules for detailed rule analysis

  • Provides both raw validation_rules_assignments and processed assigned_validation_rules

  • Includes complete campaign hierarchy and voucher generation settings

Parameters:

  • campaign_id: Campaign identifier for lookup

Identifier Requirements:

  • Campaign ID: Must start with 'camp_' prefix (e.g., "camp_X26jzR8ObD55nlwOUcr63Px0")

  • ID must correspond to existing campaign

Validation Rules:

  • Campaign ID must be valid and existing

  • ID format is case-sensitive

Examples:

  • get_campaign(campaign_id="camp_X26jzR8ObD55nlwOUcr63Px0")

Returns: JSON object containing complete campaign details including:

Core Campaign Information:

  • id: Campaign system identifier (e.g., "camp_X26jzR8ObD55nlwOUcr63Px0")

  • name: Human-readable campaign name (e.g., "Burger Deluxe Family Campaign")

  • campaign_type: Campaign type ("DISCOUNT_COUPONS", "GIFT_VOUCHERS", "LOYALTY_PROGRAM", "PROMOTION")

  • type: Campaign generation type ("STATIC", "DYNAMIC")

  • active: Boolean campaign status

  • object: Always "campaign"

Campaign Configuration:

  • auto_join: Boolean automatic customer enrollment setting

  • join_once: Boolean single enrollment restriction

  • use_voucher_metadata_schema: Boolean voucher metadata schema usage

  • protected: Boolean protection status against modifications

Voucher Template Settings:

  • voucher: Complete voucher template configuration:

    • type: Voucher type ("DISCOUNT_VOUCHER", "GIFT_VOUCHER", etc.)

    • discount: Discount configuration (e.g., {"type": "PERCENT", "percent_off": 3})

    • redemption: Usage limits (e.g., {"quantity": null} for unlimited)

    • code_config: Code generation settings:

      • length: Code length (e.g., 8)

      • charset: Character set for code generation

      • pattern: Code pattern (e.g., "########")

    • is_referral_code: Boolean referral code status

Campaign Status and Statistics:

  • vouchers_count: Total vouchers generated in campaign

  • creation_status: Campaign creation status ("DONE", "IN_PROGRESS", "FAILED")

  • vouchers_generation_status: Voucher generation status ("DONE", "IN_PROGRESS", "FAILED")

  • created_at: Campaign creation timestamp (ISO 8601)

Access and Categorization:

  • access_settings_assignments: Access control assignments with data array and total count

  • category_id: Campaign category identifier (null if uncategorized)

  • categories: Array of campaign categories (empty if uncategorized)

Validation Rules (Enhanced):

  • assigned_validation_rules: Processed validation rules with detailed conditions (added by this tool) See "Validation Rules Structure" section in server instructions for complete field documentation

Use Cases:

  • Analyze campaign configuration and discount mechanics

  • Understand validation rules and product requirements

  • Check campaign status and voucher generation progress

  • Review campaign hierarchy and settings

  • Get complete campaign blueprint for duplication

Raises:

  • ToolError: If campaign not found or invalid campaign ID format

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
campaign_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses enhanced features (fetching and processing validation rules), provides detailed return structure, and mentions error raising. Rich behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is long but well-structured with clear sections. Every sentence adds value, though it could be slightly more concise. Front-loaded with main purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the single parameter and detailed output, the description is very complete. It covers purpose, parameter semantics, output structure, examples, use cases, and error handling. No gaps.

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

Parameters5/5

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

Only one parameter (campaign_id) and schema coverage is 0%, but description adds extensive meaning: identifier requirements, format ('camp_' prefix), case-sensitivity, examples, and validation rules.

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?

Description clearly states 'Retrieve detailed information about a specific campaign by its ID.' It uses specific verb and resource. It distinguishes from siblings like list_campaigns (list all) and get_campaign_summary (summary).

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?

Description lists use cases like analyzing campaign configuration and validation rules. While it doesn't explicitly say when not to use or contrast with alternatives, the context is clear enough for an agent to decide.

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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