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Agent.ai MCP Server

Agent.ai MCP Server

by OnStartups
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JavaScript
Remote

prospect_research_lookup_prospect_by_email

Resolve an email address to a LinkedIn profile. Returns name, title, company, career history, and skills, cached for 30 days.

Instructions

Resolves an email address to a LinkedIn person profile via Fiber.ai. Returns name, title, company, LinkedIn URL, career history, skills. Cached for 30 days.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "email": "<email>",
      "output_variable_name": "<output_variable_name>"
    },
    "name": "prospect_research_lookup_prospect_by_email"
  }
}

Input Schema

NameRequiredDescriptionDefault
emailYesThe email address to look up (e.g. 'jane@acme.com').
output_variable_nameYesVariable name to store the lookup result.email_lookup

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations provided, so the description carries full burden. It discloses caching duration and return fields, but does not mention authentication requirements, what happens on invalid/not-found emails, or any rate limits. Adequate but leaves gaps.

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 with all critical info upfront: action, source, returns, caching. No filler words; every sentence adds value.

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 no output schema, the description adequately describes return fields and caching. However, it lacks error handling details or mention of data freshness beyond caching. For a simple lookup tool, this is mostly complete.

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%, and the description adds value by explaining cache behavior and the nature of returned data (career history, skills). This goes beyond the schema descriptions, which are minimal.

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 resolves an email to a LinkedIn profile via Fiber.ai, listing specific return data (name, title, etc.). This distinguishes it from siblings like 'search_linkedin_people' or 'get_linkedin_profile', which do not emphasize email resolution.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for email-based lookups and mentions caching (30 days), but lacks explicit guidance on when to prefer this tool over alternatives (e.g., 'search_linkedin_people', 'enrich_person'). No contraindications or context for when not to use it.

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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