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databar-ai

Databar MCP Server

Official
by databar-ai

get_enrichment_details

Retrieve parameters, response fields, pricing, and data source for a specific enrichment to prepare before execution.

Instructions

Get detailed information about a specific enrichment, including all required and optional parameters, response fields, pricing, and data source. Use this before running an enrichment to understand what parameters are needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
enrichment_idYesThe ID of the enrichment to get details for
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It clearly states that the tool returns required/optional parameters, response fields, pricing, and data source. This gives a good picture of the output content. It does not mention that it is read-only, but that is implied by 'get' and the description of returning information.

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?

The description consists of two concise sentences. The first sentence states what the tool does and what it returns. The second sentence gives a clear usage scenario. No extraneous information.

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 tool has only one parameter and no output schema, the description is fairly complete. It explains what the tool returns (parameters, response fields, pricing, data source). It could mention that the enrichment_id must exist or that the tool is read-only, but these are minor gaps.

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?

There is only one parameter, and the schema already provides a clear description: 'The ID of the enrichment to get details for'. The tool description does not add any additional meaning beyond what the schema already provides. With 100% schema coverage, a baseline of 3 is appropriate.

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 gets detailed information about a specific enrichment, listing included content (parameters, response fields, pricing, data source). It provides a usage hint ('Use this before running an enrichment') that distinguishes it from sibling tools like run_enrichment, which actually execute enrichments.

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 explicitly says 'Use this before running an enrichment to understand what parameters are needed.' This is strong positive guidance on when to use the tool. However, it does not explicitly state when not to use it or mention alternative tools by name.

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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