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composable-delivery

Snowfakery MCP Server

run_recipe

Run a Snowfakery recipe to generate fake data. Returns generated output and artifact URIs with options for output format, repetition, and preview control.

Instructions

Run a Snowfakery recipe and generate fake data.

Executes the recipe and returns generated output along with artifact URIs. The complete output is always written to disk and available via the returned resource URI regardless of capture_output - that setting only controls how much of it also comes back inline in this response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repsNoNumber of times to repeat the recipe
optionsNoUser options (--option key=value equivalent)
recipe_pathNoPath to recipe file (relative to workspace)
recipe_textNoRecipe YAML content as string
strict_modeNoFail on undefined field references
output_formatNoOutput format (txt, json, csv, sql, dot, svg, etc.)txt
target_numberNoGenerate until table reaches count {"table": "X", "count": N}
validate_onlyNoOnly validate, don't generate
capture_outputNoHow much generated output to include inline, in addition to the resource: "preview" (default) is a small preview plus output_bytes/record_count so you know how much data exists without paying to see all of it; "full" is the complete output inline, up to the server's max-capture-chars limit; "none" omits inline text entirely.preview
plugin_optionsNoPlugin configuration options
generate_continuationNoCreate continuation file for resuming

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Discloses that complete output is always written to disk and that capture_output only controls inline return, adding context beyond annotations. However, does not mention behavior on re-execution or effects on existing data.

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?

Two concise paragraphs: first states purpose, second adds a critical nuance about output persistence. No redundant or irrelevant content.

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?

Covers the key behavior (output always to disk, capture_output modes) for a tool with 11 parameters, 0 required, and an output schema. Leaves artifact URI details implicit but acceptable given output schema.

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?

Schema coverage is 100% with detailed parameter descriptions; the tool description adds no additional meaning beyond summarizing capture_output behavior. Baseline score of 3 applies.

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 explicitly states 'Run a Snowfakery recipe and generate fake data' with specific verb and resource, and distinguishes from siblings like analyze_recipe and validate_recipe by focusing on execution and output generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives; lacks when-to-use or when-not-to-use instructions. The description explains capture_output modes but does not direct the agent to choose this tool over others.

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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