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Bauplan MCP Server

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by BauplanLabs

create_branch

Create a new branch in the Bauplan data catalog from a specified reference to enable isolated data operations.

Instructions

Create a new branch in the user's Bauplan data catalog using a branch name, returning a confirmation.

Args: branch: Name of the new branch to create. Must follow the format <username.branch_name>. from_ref: Reference (branch/commit) to create the branch from. Can be either a branch name or a hash that starts with "@" and has 64 additional characters.

Returns: BranchCreated: Object indicating success/failure with branch details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
branchYes
from_refYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
createdYes
nameNo
hashNo
messageNo
Behavior2/5

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

With no annotations provided, the description should fully disclose behaviors. It mentions creation and confirmation but does not address error handling (e.g., what happens if branch already exists or from_ref is invalid), idempotency, permissions, or side effects. The format constraints for parameters are helpful but insufficient.

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?

The description is concise, with a clear opening sentence and structured Args/Returns sections. Every sentence adds value, and there is no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (2 required parameters, output schema), the description covers parameter formats and return type. However, it lacks usage context (e.g., when to use vs. other tools) and behavioral details (e.g., idempotency, failure modes), which limits completeness for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must provide parameter meaning. It does so: 'branch' must follow format <username.branch_name>, and 'from_ref' can be a branch name or hash starting with '@' with 64 characters. This adds significant value beyond the raw schema.

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?

The description clearly states the tool creates a new branch in the Bauplan data catalog, specifies the branch name format <username.branch_name>, and returns a confirmation. This is specific and distinguishes it from sibling tools like create_table or create_namespace.

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?

The description explains what the tool does but does not explicitly mention when to use it vs alternatives, nor does it provide prerequisites or conditions (e.g., when to create a new branch vs. using an existing one). Usage is implied through the parameter description.

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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