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databar-ai

Databar MCP Server

Official
by databar-ai

run_waterfall

Execute a waterfall enrichment by sequentially trying multiple providers until one succeeds, subject to spending limits. Requires a waterfall identifier and parameters.

Instructions

Execute a waterfall enrichment that tries multiple providers until one succeeds. Subject to spending limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waterfall_identifierYesThe identifier of the waterfall to run (e.g., "email_getter")
paramsYesParameters required by the waterfall
provider_idsNoOptional: Specific provider IDs to use (default: uses all in cost-optimized order)
email_verifierNoOptional: Email verifier enrichment ID to verify results
Behavior3/5

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

Description discloses that it tries multiple providers until one succeeds and is subject to spending limits, which provides some behavioral context. However, with no annotations, it lacks details on side effects, error handling, or whether it modifies data.

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?

Two sentences, no unnecessary words. The first sentence captures the core purpose. Slightly more structured could improve readability, but it's concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multiple providers, spending limits, nested params), the description is too brief. It doesn't explain return values, error scenarios, or how spending limits are enforced, leaving significant 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?

Input schema has 100% description coverage, so parameters are already documented. The description adds no extra meaning beyond what the schema provides, meeting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it executes a waterfall enrichment that tries multiple providers until success. The verb 'execute' and resource 'waterfall enrichment' are specific. However, it does not differentiate from sibling tools like run_bulk_waterfall or run_enrichment.

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 on when to use this tool vs alternatives. It mentions spending limits but does not specify prerequisites or scenarios for choosing this over other enrichment tools.

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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