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kaistenberg

MCP Server for LinkedIn

by kaistenberg

Get Conversation

get_conversation

Retrieve a LinkedIn messaging conversation by participant username or thread ID. Provide thread_id directly to skip inbox enumeration.

Instructions

Read a specific messaging conversation.

Provide either linkedin_username or thread_id to identify the conversation.

When looked up by linkedin_username, resolution searches the messaging inbox for the participant's display name and click-visits every matching row to capture its thread ID — LinkedIn's sidebar has no anchor hrefs or thread-id attributes, so this is the only available path. Each visit selects the row in the LinkedIn UI and may mark it as read. Pass thread_id directly to skip this enumeration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
indexNo0-based selector for which thread to open when the participant has multiple threads (e.g. an organic 1-on-1 plus an InMail). Ignored when thread_id is provided. To enumerate thread IDs first, call search_conversations.
thread_idNoLinkedIn messaging thread ID
linkedin_usernameNoLinkedIn username of the conversation participant

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

The description discloses the non-obvious behavior of click-visiting rows and potential read-marking: 'Each visit selects the row in the LinkedIn UI and may mark it as read.' This goes well beyond the sparse openWorldHint annotation.

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 compact with three sentences, front-loading the purpose and delivering essential caveats without redundancy.

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?

Given the tool's complexity (two lookup paths and side effects), the description covers usage, rationale, and risks, making it self-sufficient for invocation.

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?

While the schema already covers all parameters (100% coverage), the description adds meaningful context about when to use each identifier and the index selector's role, enriching the schema.

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 states 'Read a specific messaging conversation' with a specific verb and resource, and distinguishes between username and thread_id lookup modes, setting it apart from siblings like search_conversations and get_inbox.

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?

Provides explicit guidance on parameter selection: 'Provide either linkedin_username or thread_id' and 'Pass thread_id directly to skip this enumeration.' However, it doesn't explicitly name alternatives or exclusion criteria, relying on schema for cross-reference.

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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