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explorium-ai

Explorium AgentSource MCP Server

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by explorium-ai

enrich_prospects_profiles

Get detailed profile information for prospects including demographics, location, LinkedIn URL, current role, work history, education, and skills.

Instructions

Get detailed profile information for prospects.
Returns:
- Full name and demographic details (age group, gender)
- Location information (country, region, city)
- LinkedIn profile URL
- Current role details:
  - Company name and website
  - Job title, department and seniority level
- Work experience history:
  - Company names and websites
  - Job titles with role classifications
  - Start/end dates
  - Primary role indicator
- Education background:
  - Schools attended with dates
  - Degrees, majors and minors
- Skills and interests when available

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prospect_idsYesList of up to 50 Explorium prospect IDs from match_prospects
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral traits. It only lists return fields and mentions the input parameter, but fails to disclose whether the operation is read-only, requires authentication, has rate limits, or produces side effects. This is insufficient for safe tool selection.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description starts with a clear purpose statement but is quite long due to a bullet list of return fields. While structured, it contains redundant phrasing and could be more concise. It is adequate but not exemplary.

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's simplicity (one parameter, no output schema), the description thoroughly documents the return values across various categories (demographics, location, LinkedIn, work history, education, skills). This compensates for the missing output schema, though it lacks usage and behavioral context.

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 description coverage is 100% with a clear description for prospect_ids: 'List of up to 50 Explorium prospect IDs from match_prospects'. The tool description does not add new meaning beyond the schema, so a baseline score 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 profile information for prospects and enumerates specific fields like full name, demographics, location, LinkedIn URL, current role, work history, education, and skills. It distinguishes from sibling tools which focus on businesses or other aspects (e.g., enrich_businesses_firmographics, enrich_prospects_contacts_information), making its purpose unambiguous.

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?

The description provides no guidance on when to use this tool versus alternatives (e.g., enrich_prospects_contacts_information, enrich_prospects_linkedin_posts). It does not mention prerequisites, limitations, or exclusions, leaving the agent without context for appropriate invocation.

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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