Snowfakery Mcp
Author, debug, and run Snowfakery recipes for realistic Salesforce-style test datasets without leaving your agent session.
Overview
io.github.composable-delivery/snowfakery-mcp is a MCP server for the Ship phase that authors, analyzes, debugs, and runs Snowfakery test-data recipes.
What is this MCP server?
- Author and edit Snowfakery YAML recipes from the agent
- Analyze and debug recipe structure before generation runs
- Execute Snowfakery generation via MCP (PyPI snowfakery-mcp v0.0.6)
- stdio transport for local dev and CI-adjacent workflows
- Composable Delivery open-source server on GitHub
- PyPI package snowfakery-mcp version 0.0.6
- stdio MCP transport
- Capabilities: author, analyze, debug, and run recipes
Community signal: 4 GitHub stars.
What problem does it solve?
Integration tests need believable linked records, but writing and debugging Snowfakery recipes by hand burns solo-builder time.
Who is it for?
Builders on Salesforce-style stacks or Snowfakery-based pipelines who want agent-assisted recipe development and execution.
Skip if: Teams with no Snowfakery workflow who only need generic Faker snippets or one-off SQL seeds.
What do I get? / Deliverables
Your agent can iterate on recipes, validate structure, and run generation so sandboxes and CI fixtures stay rich and consistent.
- Reviewed and runnable Snowfakery recipe files
- Debug feedback on recipe relationships and fields
- Generated datasets for dev sandboxes or automated tests
Recommended MCP Servers
Journey fit
Synthetic data for integration and E2E tests lands hardest in Ship when you prove the product works; recipes are also built during Build integrations work. Snowfakery is primarily a test-data and recipe execution tool—testing subphase matches authoring, analyzing, debugging, and running recipes.
How it compares
Snowfakery recipe MCP integration, not a generic database seeding skill or live Salesforce admin API.
Common Questions / FAQ
Who is io.github.composable-delivery/snowfakery-mcp for?
Developers using Snowfakery for synthetic data—often in Salesforce or composable delivery contexts—who want MCP tools in their coding agent.
When should I use io.github.composable-delivery/snowfakery-mcp?
Use it when building or fixing recipes, debugging relationship errors, or generating datasets before integration or E2E test runs.
How do I add io.github.composable-delivery/snowfakery-mcp to my agent?
Install the snowfakery-mcp package from PyPI, configure a stdio MCP server entry in your agent config, and ensure Snowfakery runtime dependencies are available locally.