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herohunt-ai

herohunt-mcp

Official
by herohunt-ai

people_search

Search for people profiles using natural language queries across LinkedIn, GitHub, and StackOverflow. Optionally enrich LinkedIn profiles with contact information.

Instructions

Search for people profiles using natural language across LinkedIn, GitHub, and StackOverflow. Credits: 1 per reserved slot up front (default maxResults=100). Enrichment: +1 credit per LinkedIn profile with contact info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number.
queryNoNatural language query. Required for new searches.
enrichNoRequest email/phone enrichment for LinkedIn profiles.
filtersNoStructured filters.
per_pageNoResults per page (max 50, default 10).
searchIdNoExisting search ID for pagination.
timeoutMsNoMax wait time in ms.
maxResultsNoMax profiles to collect (default 100). Charged up front.
includeLogsNoInclude pipeline trace logs.
returnAllProfilesNoReturn all matched profiles, not just current page.
Behavior3/5

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

With no annotations, the description discloses credit costs and default maxResults, but fails to mention timeout behavior, error handling, or that searchId is used for pagination. Some behavioral traits are missing.

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?

The description is concise with two sentences covering purpose and cost. It is front-loaded but could be slightly more structured to include usage tips without becoming verbose.

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 10 parameters, no output schema, and no annotations, the description omits important details about return values, pagination, and error states. It is incomplete for a tool of this complexity.

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 coverage is 100%, so the description adds limited value beyond schema descriptions. It ties credits to maxResults but does not explain relationships between parameters like query and searchId.

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 searches for people profiles using natural language across specific platforms (LinkedIn, GitHub, StackOverflow), distinguishing it from the sibling 'people_search_paginate' which likely handles pagination.

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 provides cost context (credits per reserved slot and enrichment), but does not explicitly state when to use this tool vs alternatives like 'people_search_paginate'. It lacks when-not-to-use guidance.

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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