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databar-ai

Databar MCP Server

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by databar-ai

run_table_enrichment

Triggers a specified enrichment or waterfall to process rows in a table. Supports running on all rows, only empty rows, or rows with errors.

Instructions

Trigger an enrichment or waterfall to run on a table. By default runs on all rows. Optionally specify row_ids to run on specific rows, and run_strategy to control row selection. Works for both enrichments (from add_table_enrichment) and waterfalls (from add_table_waterfall). Subject to spending limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_uuidYesThe UUID of the table
enrichment_idYesThe table enrichment/waterfall ID to run (returned by add_table_enrichment or add_table_waterfall)
row_idsNoOptional: specific row IDs to process. When omitted, processes all rows.
run_strategyNoWhich rows to process: 'run_all' (default) runs every row, 'run_empty' skips rows that already have a result, 'run_errors' reruns only rows that ended with an error.
Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It mentions triggering runs and spending limits but lacks details on asynchronicity, return value, or handling of concurrent runs. Moderate transparency.

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?

Two concise sentences: first states core action, second elaborates on options and constraints. No filler, front-loaded, easy to parse.

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 the parameter set and lack of output schema, the description adequately explains purpose and optional parameters. It references spending limits, which is a key constraint. Missing details on execution behavior are minor given the tool's simplicity.

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 100%, so baseline is 3. The description adds context by explaining default behavior for row_ids and run_strategy, and clarifying that the tool works for both enrichments and waterfalls, adding value beyond the 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 triggers an enrichment or waterfall to run on a table, specifying the action and resource. It distinguishes from siblings that run on specific enrichments/waterfalls (like run_enrichment, run_waterfall) by emphasizing it operates on a table.

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?

The description explains default behavior (runs all rows), optional parameters (row_ids, run_strategy), and applicable types (enrichments and waterfalls). It also mentions spending limits, guiding appropriate use. However, it could explicitly contrast with bulk-run siblings.

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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