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Fresh Linkedin Profile Data MCP Server

Fresh Linkedin Profile Data MCP Server

by BACH-AI-Tools
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Find Custom Headcount

find_custom_headcount

Get the number of employees at a company that match your custom criteria. Use this headcount data to target the right contacts or analyze company structure.

Instructions

Discover the count of employees within a specific company who meet designated criteria. 1 credit per call.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "find_custom_headcount"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations, the description carries full behavioral burden, but it only adds the credit cost and the fact that a count is returned. It does not disclose output format, what happens when no employees match, how company or criteria are specified, or any side effects.

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 short sentences with no filler: the functional statement comes first, and the credit cost is a separate, useful detail. Every word contributes.

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?

Despite low complexity, the description is incomplete for an agent: it references a 'specific company' and 'designated criteria' but the schema has no parameters, and no output schema explains the return value. An agent cannot confidently infer how to invoke this tool correctly.

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?

There are zero parameters in the input schema, so the description is not required to document parameters. It still adds context by emphasizing the company/criteria qualification, though it leaves the mechanism for supplying those unspecified.

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?

The description states a clear action and resource: discovering the count of employees within a specific company who meet designated criteria. It is specific enough to separate it from many sibling company/lead tools, though it does not explicitly differentiate it from the sibling named 'count' or clarify what 'designated criteria' means.

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 is given about when to use this tool versus the many sibling search/count tools, and no exclusions or prerequisites are mentioned. The description only implies the use case through its purpose statement.

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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