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dlt-hub

dltHub-AI-workbench

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by dlt-hub

get_row_counts

Retrieve row counts for all data tables in a pipeline to monitor data volume and completeness.

Instructions

Get row counts for all data tables in a pipeline.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
output_formatNoOutput format: 'markdown' or 'jsonl'markdown
pipeline_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations provided; description does not mention read-only nature, performance impact, or any side effects. Basic transparency about safety is missing.

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?

Very concise single sentence, which is efficient. However, it could include a bit more context without becoming verbose.

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 an output schema exists, return values are covered. But description lacks details like what constitutes a 'data table', ordering, or limits. Adequate but not rich.

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

Parameters2/5

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

Schema coverage is 50% (only output_format has description). The description adds no detail on pipeline_name (e.g., format, source) and does not clarify how it identifies the pipeline. Minimal added value beyond 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 'Get row counts for all data tables in a pipeline' clearly states the verb (get) and resource (row counts for all data tables) and distinguishes it from sibling tools like list_tables or get_table_schema.

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?

No explicit guidance on when to use this tool vs alternatives like execute_sql_query or list_tables. The description implies use for row counts, but lacks exclusions or context.

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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