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BauplanLabs

Bauplan MCP Server

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by BauplanLabs

plan_table_creation

Creates a table import plan from S3 parquet files by scanning schemas and generating a YAML plan, returning a job ID for tracking.

Instructions

Generate a YAML schema plan for importing a table from an S3 URI in the user's Bauplan data catalog returning a job ID for tracking). Create a table import plan from an S3 location.

This operation will attempt to create a table based of schemas of N parquet files found by a given search uri. A YAML file containing the schema and plan is returned and if there are no conflicts, it is automatically applied.

Args: table: Name of the table to plan creation for. search_uri: S3 URI to search for parquet files. namespace: Optional namespace (defaults to "bauplan"). branch: Optional branch name. partitioned_by: Optional partitioning column. replace: Optional flag to replace existing table.

Returns: TablePlanCreated: Object indicating success/failure with job tracking details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
search_uriYes
namespaceNo
branchNo
partitioned_byNo
replaceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
table_nameYes
search_uriYes
successYes
messageYes
namespaceYes
branchYes
Behavior3/5

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

No annotations provided, so the description bears full burden. It discloses that the tool attempts to create a table based on schemas, returns a plan, and auto-applies if no conflicts. However, it does not detail error handling, authorization needs, or what happens to 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with a run-on sentence containing a misplaced parenthesis. The structured 'Args' section is helpful, but the overall text could be more concise and better organized.

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 an output schema exists, the description covers the main purpose, parameters, and outcome. However, it lacks details on error scenarios (e.g., conflicts) and how to proceed if auto-application fails. Still fairly complete for a planning tool.

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 description coverage is 0%, but the description provides clear, concise explanations for all 6 parameters in the 'Args' section, adding meaning beyond the raw schema. This compensates well for the lack of schema-level descriptions.

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 generates a YAML schema plan for importing a table from S3, and distinguishes it from siblings like 'apply_table_creation_plan' by noting automatic application if no conflicts. The verb 'plan' and resource 'table creation' are specific.

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 implies usage for planning before applying, but does not explicitly guide when to use this tool versus 'create_table' or 'apply_table_creation_plan'. It mentions automatic application, leaving uncertainty about conflict handling.

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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