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Teradata MCP Server

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by Teradata

dba_featureUsage

Retrieve user feature usage metrics for a specified date range. Optionally materialize the result as a volatile table.

Instructions

Get the user feature usage metrics for a specified date range.

Arguments: start_date - The start date for the query range in YYYY-MM-DD format. end_date - The end date for the query range in YYYY-MM-DD format. persist - If True, materializes result as a volatile table and returns table name

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateYesThe start date for the query range in YYYY-MM-DD format.
end_dateYesThe end date for the query range in YYYY-MM-DD format.
persistNoIf True, materializes result as a volatile table and returns table name
Behavior3/5

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

No annotations exist, so the description must disclose behavioral traits. It does mention the 'persist' parameter's side effect of materializing results as a volatile table, which is a key behavioral detail. However, it lacks information about other traits like authorization needs or rate limits.

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?

The description is concise and front-loaded with the purpose, followed by a clear parameter list. However, it redundantly repeats the parameter descriptions from the schema, which slightly reduces efficiency.

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?

There is no output schema, so the description should explain the return structure. It only hints at the output for the persist case. Additionally, 'user feature usage metrics' is not defined, leaving some ambiguity about the expected results.

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

Parameters3/5

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

Schema coverage is 100%, meaning the input schema already describes all parameters. The description repeats these descriptions without adding new meaning or context, so it meets the baseline but adds no extra value.

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 retrieves 'user feature usage metrics' for a date range, with a specific verb 'Get' and resource. The name 'dba_featureUsage' also matches, and there are no sibling tools with similar functionality, so it is well distinguished.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives, such as other dba_ tools. The description only explains what it does, not the context or conditions for its use.

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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