Reach MCP — LinkedIn for AI agents

list_connections

Read-onlyIdempotent

List LinkedIn 1st-degree connections for an account (Voyager dash connections), paged with start/count — same payload as GET /api/linkedin/{account_id}/connections. Use to walk the account's own 1st-degree network; for pending invitations use list_sent_invitations or list_received_invitations; for search use scrape_search.

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": {
      "account_id": "<account_id>"
    },
    "name": "list_connections"
  }
}

Input Schema

Name	Required	Description
`count`	No	Page size.
`start`	No	Pagination offset, 0-based.
`account_id`	Yes	Reach id of the LinkedIn account to act on, from list_accounts.

Output Schema

Name	Required	Description
`count`	No	Requested page size.
`items`	No
`start`	No	Offset used for this request.
`total`	No	Total connections reported by LinkedIn ``paging.total``.
`next_start`	No	If set, use as the ``start`` query param for the next page (Kanbox-style paging).

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context: pagination via start/count and payload parity with the raw Voyager endpoint. It does not discuss rate limits or quota impact, which keeps it from a 5.

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?

Three tight clauses: what it lists, how it pages, and where to go instead. The routing information is front-loaded and no sentence is redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists so return values need not be explained, annotations carry the safety profile, and the schema fully documents parameters. The description completes the picture by clarifying scope and pointing to sibling tools for adjacent cases.

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% – start, count and account_id are each documented in the schema. The description only restates that results are paged with start/count, adding no format, bounds, or default detail beyond the schema. Baseline 3 applies.

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?

States a specific verb+resource with scope: 'List LinkedIn 1st-degree connections for an account', and even names the underlying payload parity with GET /api/linkedin/{account_id}/connections. It clearly distinguishes itself from the invitations and search siblings by naming them.

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

Usage Guidelines5/5

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

Explicitly says when to use it ('walk the account's own 1st-degree network') and routes to alternatives by condition: pending invitations → list_sent_invitations/list_received_invitations, search → scrape_search. Nothing is left to inference.

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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