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Python
Hybrid

Create Study Recipe

create_study_recipe

Freeze a study recipe without fitting to lock validated model inputs and preserve lineage; use source revision IDs when deriving from published same-study recipes.

Instructions

Freeze a recipe without fitting. Supply source_revision_id when deriving from a published same-study recipe to retain influence ancestry. Optional expected_content_hash binds the validated effective inputs; a mismatch returns 409 and requires a fresh preview. Specification kind api_mmm has request containing create_model API fields; model_snapshot has model_hash and is review-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
reasonYes
study_idYes
specificationYesBackend recipe envelope. api_mmm requires request (a create_model body; model_type must be mmm, unknown config keys are rejected, channel_map and var_model_hash are rejected); model_snapshot requires model_hash and is review-only (launch refused, lineage unknown). Smart priors and VAR recipes are available only through the authoring-draft tools. Unknown fields are forwarded for backend validation. Every saved revision's read-time inspection carries lineage: the dataset origin recorded when the model was built, checked for availability now and never inferred after the fact.
source_revision_idNo
expected_content_hashNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.0
    • changedInput schema / properties / specification / description
      Previous value: -"Backend recipe envelope. api_mmm requires request (a create_model body; model_type must be mmm, config.auto_prior must be false, channel_map and var_model_hash are rejected); model_snapshot requires model_hash and is review-only (launch refused, lineage unknown). Smart priors and VAR recipes are available only through the authoring-draft tools. Unknown fields are forwarded for backend validation. Every saved revision's read-time inspection carries lineage: the dataset origin recorded when the model was built, checked for availability now and never inferred after the fact."New value: +"Backend recipe envelope. api_mmm requires request (a create_model body; model_type must be mmm, unknown config keys are rejected, channel_map and var_model_hash are rejected); model_snapshot requires model_hash and is review-only (launch refused, lineage unknown). Smart priors and VAR recipes are available only through the authoring-draft tools. Unknown fields are forwarded for backend validation. Every saved revision's read-time inspection carries lineage: the dataset origin recorded when the model was built, checked for availability now and never inferred after the fact."
  2. Changed1 schema field changedv0.8.2
    • changedInput schema / properties / specification / description
      Previous value: -"Backend recipe envelope. api_mmm requires request (a create_model body; model_type must be mmm, config.auto_prior must be false, channel_map and var_model_hash are rejected); model_snapshot requires model_hash and is review-only (launch refused, lineage unknown). Smart priors and VAR recipes are available only through the authoring-draft tools. Unknown fields are forwarded for backend validation."New value: +"Backend recipe envelope. api_mmm requires request (a create_model body; model_type must be mmm, config.auto_prior must be false, channel_map and var_model_hash are rejected); model_snapshot requires model_hash and is review-only (launch refused, lineage unknown). Smart priors and VAR recipes are available only through the authoring-draft tools. Unknown fields are forwarded for backend validation. Every saved revision's read-time inspection carries lineage: the dataset origin recorded when the model was built, checked for availability now and never inferred after the fact."
  3. Changed1 schema field changedv0.7.3
    • changedInput schema / properties / specification / description
      Previous value: -"Backend recipe envelope. api_mmm requires request; model_snapshot requires model_hash and is review-only. Unknown fields are forwarded for backend validation."New value: +"Backend recipe envelope. api_mmm requires request (a create_model body; model_type must be mmm, config.auto_prior must be false, channel_map and var_model_hash are rejected); model_snapshot requires model_hash and is review-only (launch refused, lineage unknown). Smart priors and VAR recipes are available only through the authoring-draft tools. Unknown fields are forwarded for backend validation."
  4. Addedv0.5.0

TDQS

A3.7/5.0
Behavior4/5

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

Annotations cover the write/non-idempotent/non-destructive profile, and the description adds real behavior beyond them: a hash mismatch returns 409 and forces a fresh preview, and model_snapshot is review-only (launch refused, lineage unknown). It does not state permission/auth requirements, but the mutation semantics and failure mode are well disclosed.

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?

Four dense sentences, each carrying distinct information, with the core purpose front-loaded. No filler, though the spec-kind sentence packs multiple constraints into one line.

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?

For a write tool with a nested spec object, an output schema (so returns need no explanation), and rich annotations, the description covers the key branching (kind selection), the ancestry concern, and the 409 failure path. Only the untouched scalar params keep it from being fully complete.

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 description coverage is only 17%, so the description must compensate, and it does add meaning for source_revision_id and expected_content_hash that the schema lacks (titles only). It leaves name, reason, and study_id unexplained, so roughly half the parameters remain undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a specific verb+resource: 'Freeze a recipe without fitting', which pins down the operation and distinguishes it from fitting/authoring. It doesn't name the sibling tools it replaces (create_recipe_draft, publish_recipe_draft), but 'freeze' vs 'draft'/'fit' is inferable.

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

Usage Guidelines3/5

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

Gives conditional guidance for source_revision_id ('when deriving from a published same-study recipe') and for choosing between api_mmm and model_snapshot kinds. However, it never states when to use this tool versus create_recipe_draft, revise_study_recipe, or refreeze_recipe_revision, so tool-selection guidance is only implied.

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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