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databar-ai

Databar MCP Server

Official
by databar-ai

run_enrichment

Execute data enrichment with provided parameters, automatically handling async polling and returning cached results. Supports pagination for multiple pages.

Instructions

Execute a data enrichment with the provided parameters. Automatically handles async execution and polling, returning final results. Results are cached for 24 hours to reduce costs. Subject to spending limits (DATABAR_MAX_COST_PER_REQUEST, DATABAR_MIN_BALANCE). For paginated enrichments, use the pages parameter to fetch multiple pages (each page is billed separately).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
enrichment_idYesThe ID of the enrichment to run
paramsYesParameters required by the enrichment (e.g., {"email": "test@example.com"})
skip_cacheNoSkip cache and fetch fresh data (default: false)
pagesNoNumber of pages to fetch for paginated enrichments (default: 1, max: 100). Each page is billed separately. Use get_enrichment_details to check if pagination is supported.
Behavior4/5

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

With no annotations, the description covers async execution, polling, caching (24 hours), spending limits, and per-page billing. Missing details on error handling or partial results, but major behaviors are disclosed.

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?

Four concise sentences, each adding necessary information. No fluff, well-structured with key points front-loaded.

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?

For a tool with 4 parameters and no output schema, it covers usage, caching, limits, and pagination. Could mention return format, but overall complete enough for agent to use correctly.

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%, baseline 3. Description adds value by explaining pages billing, caching skip behavior, and hinting at enrichment_details for pagination support. Enhances understanding beyond schema 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 executes a data enrichment, handles async execution and polling, and returns final results. It distinguishes from siblings by focusing on a specific enrichment ID rather than table-level enrichment.

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?

Provides actionable guidance: mentions caching (reduces costs), spending limits, and pagination usage with reference to get_enrichment_details for checking pagination. Lacks explicit comparison to run_table_enrichment but still offers good 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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