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Faker System

Station’s Faker system generates realistic mock data using AI, enabling safe development and testing without production credentials.

Why Fakers?

Quick Start

Via MCP Tool

Station uses the faker_create_standalone tool to set up the faker.

Via CLI

How It Works

Fakers are MCP servers that:
  1. Receive tool calls from agents
  2. Use AI to generate contextually appropriate responses
  3. Return realistic mock data

Creating Fakers

Programmatic

In template.json

Configuration

Goal/Instruction

The goal or ai-instruction guides the AI in generating appropriate data: Good:
Too vague:

Tool Definitions

Define tools that match your real MCP server’s interface:

Examples

Infrastructure Monitoring

Security Scanning

Cost Analysis

Incident Response

Using Fakers in Agents

Assign to Agent

In template.json

Faker vs Real MCP Server

Development with Faker

Production with Real Server

Use different template.json files per environment to swap between faker and real.

Advanced Configuration

Persistence

By default, fakers don’t persist data between calls. For stateful scenarios:

Auto-sync

Faker configurations can auto-sync to your environment:

Debug Mode

Enable verbose logging to see AI prompts and responses:

Testing Agents with Fakers

Generate Test Scenarios

Run Evaluation

Best Practices

  1. Match real schemas - Faker tool schemas should match your real MCP servers
  2. Be specific in goals - Detailed instructions produce more realistic data
  3. Include edge cases - Mention error conditions and anomalies in goals
  4. Version your fakers - Keep faker configs in Git alongside agents
  5. Test transitions - Ensure agents work with both faker and real data

Troubleshooting

Generic/Unrealistic Data

Problem: Faker returns too generic data Solution: Make the goal more specific:

Schema Mismatch

Problem: Agent expects different data format Solution: Define explicit output schema in tool definition:

Slow Responses

Problem: Faker takes too long Solution:
  1. Use a faster model for fakers
  2. Simplify the goal
  3. Cache common responses

Next Steps