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LinkedIn MCP Server and API by Cotlet

by dev.cotlet
OverviewDiscussionsAdmin

List connections in LinkedIn

linkedin_list_connections
Read-onlyIdempotent

List connections in the user's own LinkedIn account from ChatGPT, Claude or any AI agent. Returns how to do it in LinkedIn step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's LinkedIn Cloud Browser. LinkedIn's official API: LinkedIn has an official API, but most of it needs an approved application (checked 6 Nov 2026).

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": "linkedin_list_connections"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description reinforces this with 'opens nothing by itself', which is the single most important behavioral fact for an agent that might expect a connection list. It adds the official-API constraint (approval needed, checked 6 Nov 2026) as rationale for the browser route. It leaves some ambiguity about whether any actual connection data is ever returned versus only prose guidance.

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?

Three compact sentences that front-load the core caveat ('opens nothing by itself') right where an agent needs it. The trailing note about LinkedIn's official API is slightly tangential but earns its place by justifying the browser-based approach.

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?

With no output schema, the description carries the burden of describing the return value, and it does so concretely ('screens, buttons, fields'). The prerequisite chain (Cloud Browser session, official API approval) is covered. What's missing is any statement about what the agent should do if the user has no active Cloud Browser session.

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?

The tool takes zero parameters and the schema is closed with additionalProperties=false, so there is no parameter semantics to explain. Baseline 4 applies, and nothing in the description misrepresents the parameterless surface.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The headline verb 'List connections' suggests a data-fetch operation, but the second sentence reveals the tool actually returns a step-by-step procedure rather than connections. That reversal is disclosed, so the purpose is recoverable, but the opening clause points an agent at the wrong mental model before correcting itself, and it doesn't distinguish this from siblings like linkedin_search_people.

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

Usage Guidelines4/5

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

It explicitly states that this tool 'opens nothing by itself' and directs the agent to 'do it with the cotlet_browser_* tools in the user's LinkedIn Cloud Browser', giving a clear usage path and handoff. It stops short of naming when to prefer this over other linkedin_* siblings, so no exclusions are provided.

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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