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LinkedIn account settings

get_linkedin_account_settings

Per-account LinkedIn settings shared by every campaign and SDR process using the account: AI auto-replies (on/off, tone, instructions, pitch, booking link, reply delays, daily limit, working hours, agent, contact info), invites (on/off, tone, pitch, instructions, CTA), follow-ups (on/off, pitch, instructions, CTA) and signature (text, first_message|every_message). The first read creates defaults (pre-filled from campaign_id's agent).

Input Schema

NameRequiredDescriptionDefault
account_idYes
campaign_idNoPre-fill defaults from this campaign's agent

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose the important side effect that the first read creates defaults, pre-filled from campaign_id's agent. It does not cover permissions, rate limits, or what happens on subsequent reads, 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded around the resource and then enumerates the relevant settings groups efficiently. The long parenthetical list is dense but each item contributes to scope clarity, so it is appropriately sized rather than padded.

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?

For a two-parameter read tool with no output schema, the description gives enough context about what settings are returned and the default-creation behavior. It could be stronger by clarifying permissions or return shape, but it is largely complete for the 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 50%: campaign_id is documented in the schema, while account_id has no schema description. The description adds useful meaning by framing settings as account-level and explaining that campaign_id pre-fills defaults from that campaign's agent, but it does not document account_id format or constraints.

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 identifies a specific resource, per-account LinkedIn settings, and enumerates the settings groups it covers. It does not explicitly state the verb 'retrieves' or name the sibling update_linkedin_account_settings as the mutation alternative, so it stops short of perfect sibling differentiation.

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?

Usage is implied: read the account-level settings shared across campaigns and SDR processes. However, there is no explicit guidance on when to call this versus update_linkedin_account_settings, nor are there prerequisites or exclusions.

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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