MCP Best Practices

Practical patterns and strategies for efficient test data management with the Model Context Protocol

Integrating Seedfast MCP into your development workflow unlocks conversational database seeding - but getting the most value requires understanding how AI agents interpret your requests and how the underlying tooling works. This guide distills real-world patterns into actionable best practices that will make your test data generation faster, more reliable, and more secure.

Whether you're looking for a faker alternative that handles relational database seeding, a schema-aware data generator for complex schemas, or a constraint-aware test data solution for CI/CD pipelines, these practices will help you get the most from this PostgreSQL test data generator.

The State of Test Data Management in 2026

Before diving into practices, context matters. Recent industry research reveals significant challenges:

• 90% of organizations using commercial test data management tools rely on legacy solutions launched over 15 years ago

• Only 7% of companies report full compliance with data privacy regulations in software testing

• Data silos cost organizations $7.8 million annually in lost productivity, with employees wasting 12 hours weekly searching for information

The synthetic data generation market is projected to grow from $447 million in 2025 to $8.79 billion by 2035 - a 34.7% CAGR that reflects the urgent need for modern test data solutions. Seedfast MCP addresses this by bringing AI-powered synthetic data generation directly into developer workflows.

Why MCP? The Conversational Advantage

Traditional database seeding tools require you to context-switch: leave your IDE, write configuration files, run CLI commands, check outputs. MCP eliminates this friction by embedding seeding directly into your AI-assisted development flow.

Before MCP:

1. Stop coding
2. Open terminal
3. Remember CLI syntax
4. Write seed script or command
5. Run and check output
6. Return to coding

With MCP:

1. Ask your AI assistant: "Seed the users table"
2. Continue coding

The real power emerges when seeding becomes part of a larger conversation:

"I'm building the order history feature. Seed the orders table with related line_items and payments, then show me how the schema looks."

Your AI assistant handles the seeding, reports progress, and can immediately help you query or understand the generated data - all without breaking your flow.

Real-World Use Cases

Developer Onboarding

New team members often spend hours setting up local databases. With Seedfast MCP configured in your project's .mcp.json, onboarding becomes a conversation:

New dev: "Set up my local database with test data for the main features"
AI: Creates plan, seeds 15 tables, reports completion in 45 seconds

Combine with Docker Compose for a complete setup:

# docker-compose.yml
services:
  db:
    image: postgres:16
    environment:
      POSTGRES_DB: myapp_dev
    ports:
      - "5432:5432"

docker compose up -d
# Then in your AI assistant:
# "Connect to localhost:5432/myapp_dev and seed the core tables"

Feature Branch Isolation

Each feature branch can have its own seeded database state. When working on a new feature:

"Create a seeding plan for the notification system tables and save it as plan for PR #847"

Later, any team member reviewing the PR can:

"Execute the seeding plan for PR #847 on my local database"

This ensures everyone tests against identical data structures.

Pre-Demo Environment Setup

Before client demos or stakeholder reviews, quickly prepare consistent demo environments:

"Seed the staging database with tables: users, orders, products, payments"

The plan-then-execute pattern is essential here - review exactly what will be seeded before touching a shared environment.

Database Migration Testing

Before running migrations on production, test them against realistic data:

# 1. Create a test database
createdb migration_test

# 2. Restore production schema (without data)
pg_dump --schema-only prod_db | psql migration_test

# 3. Ask AI to seed it
# "Seed the migration_test database with all tables"

# 4. Run your migration
npm run migrate

# 5. Verify results

Load Testing Preparation

Generate large datasets for performance testing:

"Seed the analytics schema with 100,000 rows across events, sessions, and conversions tables"

Note: For very large datasets, consider running multiple focused seeding operations rather than one massive scope.

Reproducing Production Issues

When debugging production bugs, recreate similar data structures locally:

"Seed the accounts table with related subscriptions and billing_history tables"

Ephemeral CI Databases

Modern CI pipelines can spin up PostgreSQL containers per job. Combine with Seedfast for isolated, reproducible test environments:

# GitHub Actions
jobs:
  test:
    services:
      postgres:
        image: postgres:16
        env:
          POSTGRES_DB: test
        ports:
          - 5432:5432
    steps:
      - uses: actions/checkout@v4
      - name: Run migrations
        run: npm run db:migrate
      - name: Seed database
        run: |
          seedfast seed \
            --dsn "postgresql://postgres:postgres@localhost:5432/test" \
            --scope "Seed tables for integration tests" \
            --output json
      - name: Run tests
        run: npm test

Multi-Service Development

When working with microservices that share database schemas:

"I'm working on the orders service. Seed only the tables owned by the orders schema, but include the users table from the shared schema for foreign key references"

Integration with AI Coding Assistants

Seedfast MCP works with any MCP-compatible AI assistant:

• Claude Desktop: ~/.config/claude/mcp.json or claude_desktop_config.json

• Cursor: .cursor/mcp.json in project root

• Windsurf: .windsurf/mcp.json in project root

• Continue: config.json MCP servers section

• Claude Code (CLI): .mcp.json in project root

Example configuration for any assistant:

{
  "mcpServers": {
    "seedfast": {
      "command": "seedfast",
      "args": ["mcp"],
      "env": {
        "SEEDFAST_API_KEY": "${SEEDFAST_API_KEY}"
      }
    }
  }
}

Conversational Patterns That Work

Iterative refinement:

"Seed the products table"
"Now add the categories table and link them"
"Also seed inventory for those products"

Exploratory seeding:

"What tables exist in the public schema?"
"Seed just the core ones, skip anything with 'audit' or 'log' in the name"

Debugging assistance:

"The orders table seeding failed. Check the run status and tell me what went wrong"

Plan management:

"Show me all my seeding plans from today"
"Delete the old plans, keep only the one for the auth feature"

Scope Writing: The Art of Effective Prompts

The --scope parameter is how you communicate intent to Seedfast's AI engine. Better scopes produce better synthetic test data—faster.

Be Specific, Not Generic

# Too broad - seeds entire database, slow
"Seed all tables"

# Better - targets relevant subsystem
"Seed user authentication tables: users, sessions, password_resets"

Specify Relationships Explicitly

When database data with relationships matters for your tests, state them explicitly:

# Implicit relationships - AI may or may not connect them
"Seed users and orders"

# Explicit relationships - guarantees connected data
"Seed users with related orders and line items"

Use Negative Scoping for Exclusions

# Exclude sensitive or irrelevant tables
"Seed all tables in public schema except audit_logs and system_configs"

Plan-Then-Execute Pattern

For production-like environments or large datasets, always review before seeding.

Step 1: Generate Plan

Ask your AI assistant:

"Create a seeding plan for products, warehouses, and stock_levels tables"

The seedfast_plan tool returns:

• Which tables will be seeded

• Estimated row counts

• Dependency order

Step 2: Review Tables

Verify the plan includes all necessary tables and excludes sensitive ones:

Plan ID: plan_a1b2c3d4e5
Tables (3):
  - products
  - warehouses
  - stock_levels

Preview: Will seed 3 tables

Step 3: Execute with Plan ID

"Execute seeding plan plan_a1b2c3d4e5 on my database"

This ensures the exact reviewed plan executes, not a reinterpreted scope.

Idempotency for Reliable Automation

When using database seeding ci workflows, use idempotency keys to prevent duplicate runs:

{
  "dsn": "postgresql://...",
  "scope": "CI test data for PR #123",
  "idempotencyKey": "ci-seed-pr-123-users"
}

If a run with the same idempotency key exists, Seedfast returns the existing run instead of creating a duplicate. This is critical for:

• Retry-safe pipelines: Network failures won't cause double-seeding

• Parallel test jobs: Multiple jobs referencing the same seed key coordinate safely

• Audit trails: Each unique seed operation has a single, traceable run ID

Security Best Practices

Use Environment Variables for DSN

Pass database connection strings via environment rather than tool arguments:

export SEEDFAST_DSN="postgresql://user:pass@host:5432/db"

This keeps credentials out of command history, process lists, and MCP logs.

Rotate API Keys Regularly

Recommended rotation schedule:

• Development keys: Every 90 days

• CI/CD keys: Every 30 days

• Shared team keys: Avoid; use per-user keys

Separate Keys by Environment

Create distinct API keys for local development, CI/CD pipelines, staging environments, and production seeding.

Performance Optimization

Narrow Scope = Faster Seeding

Fewer tables means faster completion:

• Single table: 5-15 seconds

• 5-10 related tables: 30-60 seconds

• Full schema (50+ tables): 2-5 minutes

For development iteration, seed only what your current feature needs.

Cache Connection Metadata

Seedfast caches schema analysis during a session. Keep the MCP server running rather than restarting between operations.

Error Handling Patterns

Check Run Status for Failures

After seedfast_run, always verify completion:

"Check the status of run run_abc123"

Handle Partial Failures Gracefully

If some tables fail, the run continues with other tables. Review the summary:

Summary:
  Success: false
  Total Tables: 10
  Succeeded: 8
  Failed: 2 (orders, payments)

Investigate failed tables individually and adjust scope or fix schema issues.

MCP Resources: Beyond Tools

Seedfast MCP exposes not just tools but also resources - read-only data endpoints that AI assistants can access for context.

Available Resources:

• seedfast://plans — List all plans in current session

• seedfast://plans/{planId} — Get specific plan details

• seedfast://runs/{runId} — Get run status and progress

• seedfast://runs/{runId}/log — Stream run events as NDJSON

Anti-Patterns to Avoid

Don't Seed in Production Without Explicit Intent

Protect with separate API keys without production access, environment validation in CI/CD, and confirmation prompts in interactive contexts.

Don't Use Overly Complex Scopes

The AI interprets scope as a single coherent request. Avoid combining too many table selections and exclusions. Instead, break into multiple focused operations.

Don't Ignore Schema Migrations

Always run migrations before seeding:

- run: npm run db:migrate
- run: seedfast seed --scope "..."

The Future: MCP as Developer Infrastructure

The Model Context Protocol represents a shift in how developers interact with tooling. Instead of learning CLI flags, configuration formats, and API endpoints, you express intent in natural language and let AI assistants handle the translation.

Seedfast MCP is an early example of this pattern, but the principles apply broadly:

• Conversational interfaces reduce context-switching

• Plan-then-execute patterns provide safety without sacrificing speed

• Resource-based introspection enables AI assistants to provide intelligent suggestions

• Idempotency makes automation reliable

As more developer tools adopt MCP, your AI assistant becomes a unified interface to your entire development infrastructure.

Getting Started

1. Install Seedfast CLI: npm install -g seedfast@latest

2. Get API key: Sign up at dashboard.seedfa.st

3. Configure MCP: Add to your assistant's MCP config

4. Start seeding: Ask your AI assistant to seed your database