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Bauplan MCP Server

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by BauplanLabs

project_run

Run a data pipeline from a specified project directory and branch, returning a job ID and success/failure. Supports parameter templating and dry-run mode.

Instructions

Run a pipeline from a specified directory and data ref, returning a job ID and success/failure to the caller.

Args: project_dir: The directory of the project containing the source code files and bauplan_project.yml. ref: The ref or branch name from which to run the project. namespace: The Namespace to run the job in. If not set, the job will be run in the default namespace. parameters: Parameters for templating DAGs. Keys are parameter names, values must be simple types (str, int, float, bool). dry_run: Whether to enable or disable dry-run mode for the run; models are not materialized (defaults to False). client_timeout: Seconds to timeout (defaults to 120).

Returns: RunState: Object indicating success/failure with job Id

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_dirYes
refYes
namespaceNo
parametersNo
dry_runNo
client_timeoutNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successYes
job_idNo
Behavior3/5

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

No annotations provided, so description carries full burden. It mentions return behavior and dry_run mode, but does not disclose side effects, idempotency, auth requirements, or other behavioral traits.

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?

Description is well-structured with Args and Returns sections, but could be slightly more concise. Each section earns its place, but some sentences are verbose (e.g., parameter 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?

Given 6 parameters, 2 required, no annotations, and a described output, the description covers parameters well and mentions return type. Lacks behavioral details but is generally complete for invoking the tool.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates fully with detailed Args section explaining each parameter's purpose and defaults. Adds meaning beyond the schema for all 6 parameters.

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 runs a pipeline from a directory and data ref, returning a job ID. It distinguishes from siblings like code_run and run_query by specifying the source as a project directory.

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?

Description implies when to use (running a project pipeline), but lacks explicit guidance on when not to use or how it differs from siblings like code_run or run_query. No exclusions or alternative mentions.

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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