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Create Study

create_study

Define a research question for an existing project, including optional context, without launching models or consuming attempts.

Instructions

Create a study owned by an existing project. Does not launch models or consume attempts. question is what the study should find out (one or two sentences): stored and shown to humans, never executed, and must not contain acceptance thresholds, validation rules or run limits (those belong in a quality policy, recipe revisions and max_attempts/max_concurrent). Exploratory and reliability questions are valid. context (optional): scope, data caveats and assumptions a reader needs to interpret results. Example question: 'How much do paid search and paid social contribute to weekly sales after price, promotions and seasonality?'

Input Schema

TableJSON Schema

Name	Required	Description
`name`	Yes
`context`	No	Optional scope, data caveats and assumptions a reader needs to interpret results. Not rules; see question. Omit to leave unchanged on update; send an empty string to clear.
`question`	Yes	What the study should find out, in one or two sentences. Stored and shown to humans; never executed or used as an acceptance rule. Do not put thresholds, validation rules or fit limits here: those belong in a quality policy, recipe revisions and max_attempts/max_concurrent. Exploratory and reliability questions are valid.
`project_id`	Yes
`max_attempts`	No
`max_concurrent`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv0.6.1Sep 24, 2026

addedInput schema / properties / context

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Optional scope, data caveats and assumptions a reader needs to interpret results. Not rules; see question. Omit to leave unchanged on update; send an empty string to clear.",
+  "title": "Context"
+}

addedInput schema / properties / question / description

Added value: +"What the study should find out, in one or two sentences. Stored and shown to humans; never executed or used as an acceptance rule. Do not put thresholds, validation rules or fit limits here: those belong in a quality policy, recipe revisions and max_attempts/max_concurrent. Exploratory and reliability questions are valid."

addedInput schema / properties / question / examples

Added value: +[
+  "How much do paid search and paid social contribute to weekly sales after price, promotions and seasonality?",
+  "Which carryover and saturation choices does the data support for TV, and how sensitive are contributions to them?",
+  "Can a weekly model across all channels produce estimates that hold up on a temporal holdout?"
+]

Addedv0.5.0Sep 20, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already flag this as a non-read-only, non-idempotent create operation, and the description adds valuable behavioral detail: creation has no launch side effects, does not consume attempts, and the question is stored for humans rather than executed. No contradiction with annotations.

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?

The definition is front-loaded with the main purpose and scoping, and the example question is useful. It is a single dense paragraph without filler, though some content repeats what is already in the schema descriptions.

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 create tool with an output schema and annotations covering safety, the description covers purpose, ownership, side-effect absence, and question constraints well enough to invoke correctly. It does not fully explain max_attempts/max_concurrent semantics, but those have defaults and are named in the run-limits guidance.

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?

Only 2 of 6 parameters have schema descriptions (33% coverage), so the description needed to compensate; it does well for question and context, including what belongs elsewhere. However, max_attempts and max_concurrent are only referenced as places for run limits, and name/project_id receive no semantic detail, leaving meaningful gaps.

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?

States a specific verb ('Create'), the resource ('a study'), and the ownership constraint ('owned by an existing project'), and immediately differentiates itself from related actions by noting it does not launch models or consume attempts. This makes it clearly distinct from siblings like launch_study_run and update_study.

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

Usage Guidelines4/5

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

Provides clear guidance on what this tool is not for: it does not launch models or consume attempts, and it explicitly routes acceptance thresholds and validation rules away from the question field to quality policies, recipe revisions, and max_attempts/max_concurrent. It stops short of naming sibling tools like update_study or launch_study_run as explicit alternatives.

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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