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artik0din

MCP LinkedIn Sales Navigator

by artik0din

linkedin_export_leads

Export LinkedIn Sales Navigator leads from current search results or a specific list to JSON or CSV. Choose fields and limit up to 250 leads.

Instructions

Export leads from LinkedIn Sales Navigator search results or a specific list to JSON or CSV format

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of leads to export (default: 25, max: 250)
fieldsNoFields to include (default: fullName, firstName, lastName, title, company, location, salesNavUrl)
formatNoExport formatjson
listIdNoList ID to export from (required if source is 'list')
sourceYesExport from current search results ("current_search") or a specific list ("list")

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.2.0
    • addedInput schema / properties / listId / pattern
      Added value: +"^[A-Za-z0-9_-]+$"
  2. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the export action and format, omitting potential side effects (e.g., API rate limits, session requirements, or behavior when the source is invalid). This is a significant gap for a data-export tool.

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?

The description is a single concise sentence that front-loads the core action and scope. There is no extraneous information, and it is easy to parse at a glance.

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 the tool has five parameters, no output schema, and no annotations, the description is insufficient. It does not explain the dependency between source and listId (that listId is required when source is 'list'), nor does it describe the output structure or potential error conditions. An agent would need to infer these from the schema alone.

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%, so the schema already documents each parameter (source, listId, limit, fields, format). The tool description adds no additional meaning beyond what the schema provides, only repeating the format options. Baseline of 3 is appropriate when the schema does the heavy lifting.

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 specifies the action (export leads), the source (search results or a specific list), and the output format (JSON/CSV). It distinguishes itself from sibling tools like linkedin_search_leads (which finds leads) and linkedin_get_lead_profile (which retrieves a single profile), 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 Guidelines3/5

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

The description states the two possible sources (current_search or list) but does not provide explicit guidance on when to choose this tool over alternatives, nor does it mention prerequisites such as having an active search session or a valid list ID. Usage context is only implied, with no exclusions or alternative references.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.