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databar-ai

Databar MCP Server

Official
by databar-ai

search_enrichments

Search for data enrichments by query or category to find the right enrichment for tasks like email finding or company research. Returns ranked results with descriptions, parameters, and pricing.

Instructions

Search and discover available data enrichments. Use this to find the right enrichment for a specific task (e.g., "linkedin profile", "email finder", "company data"). Returns a list of matching enrichments with their IDs, descriptions, required parameters, and pricing. Results are sorted by recommendation rank (best options first). BYOK providers that the user has not connected are automatically excluded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query to find enrichments (e.g., "linkedin", "email verification", "company data", "job postings", "tech stack")
categoryNoOptional: Filter by category name (e.g., "Company Data", "Contact Finding", "Hiring Signals", "Tech Stack", "SEO", "Reviews")
limitNoMaximum number of results to return (default: 10)
Behavior4/5

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

No annotations, so description carries full burden. It discloses that BYOK providers not connected are excluded and results are sorted by recommendation rank. This adds important behavioral context without contradicting any structured data.

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?

Two sentences, front-loaded with purpose, each sentence adds distinct value. No redundant or missing information. Achieves optimal conciseness.

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

Completeness5/5

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

For a search tool with no output schema, the description adequately covers what the tool does, its inputs, and what it returns (IDs, descriptions, required parameters, pricing). No gaps in context.

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 provides examples for query ('linkedin', 'email verification'), explains category as optional with examples, and mentions limit default. This adds value beyond schema definitions.

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 it searches and discovers available data enrichments, with concrete examples ('linkedin profile', 'email finder'). It distinguishes itself from siblings like get_enrichment_details and run_enrichment by focusing on discovery.

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?

It explicitly says to use this to find the right enrichment for a specific task. While it doesn't list when not to use alternatives, the context of siblings like search_exporters and run_enrichment provides clear differentiation.

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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