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Retrieve a LinkedIn messaging conversation by providing the participant's username or thread ID.

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
linkedin_usernameNoLinkedIn username of the conversation participant
thread_idNoLinkedIn messaging thread ID
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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Annotations indicate readOnlyHint=true, but the description adds critical behavioral context: clicking rows may mark messages as read, and the reason (no hrefs in LinkedIn sidebar). This goes beyond annotations.

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 slightly verbose but well-structured, with each paragraph adding distinct value. It could be more concise without losing clarity.

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 name, schema, annotations, and siblings, the description fully explains the tool's purpose, usage, side effects, and references related tools. The output schema exists, so not explaining return values is acceptable.

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 input schema covers all parameters (100% coverage). The description adds contextual meaning, such as when to use each identifier and how 'index' works, which is helpful but not strictly necessary.

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 explicitly states it reads a specific messaging conversation, uses a clear verb ('Read'), and distinguishes from siblings like 'send_message' and 'search_conversations'.

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?

It provides clear guidance: provide either 'linkedin_username' or 'thread_id', explains the lookup process for username, mentions the 'index' parameter, and references sibling 'search_conversations' for enumeration.

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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