Skip to main content
Glama

get_conversation

Retrieve LinkedIn message history with a person; if not yet synced, initiates synchronization and returns the messages once ready.

Instructions

Retrieve a conversation with a LinkedIn person via standard LinkedIn messaging. Returns the conversation immediately if it has already been synced. If the conversation has not been synced yet, this tool starts a syncConversation workflow and returns its ack ({status, workflowId, operationName: "syncConversation", message}); the client should call get_workflow_result with that workflowId until completion, then call get_conversation again to read the synced messages.

Linked API actions are queued into a cloud-browser workflow and may take several minutes. The server returns immediately after starting the workflow with {status: 'pending'|'running', pendingReason, workflowId, operationName, message}. To retrieve the final result, call get_workflow_result with the returned workflowId and operationName — it will long-poll until completion or the request budget elapses, then return either the final result or another in-progress snapshot. Do not retry the original tool while a workflow is still running; that creates duplicate queued work.

A pending workflow carries pendingReason: 'queued' means it is waiting its turn behind other work on the same account and will start within minutes. 'outsideWorkingHours' means the account has configured working hours and the workflow is parked until they reopen — possibly the next working day. In that case get_workflow_result returns immediately instead of polling, message states when the window opens, and you should report that to the user rather than looping.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNoOptional ISO 8601 timestamp to only retrieve messages since this date (e.g., '2024-01-15T10:30:00Z'). If not provided, the entire conversation history will be returned.
personUrlYesThe LinkedIn URL of the person whose conversation you want to poll (e.g., 'https://www.linkedin.com/in/john-doe')
Behavior5/5

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses the asynchronous workflow, immediate-ack behavior, pendingReason semantics, working hours parking, and the correct polling procedure. It even explains the ack format and the need to re-call after workflow completion, leaving no ambiguity about the tool's side effects.

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 lengthy but every sentence serves a purpose: the first states the core function, the second explains the sync workflow, the third covers queuing and polling behavior, and the fourth handles edge cases for pending reasons. It is front-loaded with the main behavior and structured logically, making it easy to parse.

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 (async workflow), lack of output schema, and absence of annotations, the description covers all necessary context: immediate vs. deferred return, ack format, polling via get_workflow_result, retry prohibition, and pendingReason handling. No critical gaps remain for an agent to invoke the tool and handle the response correctly.

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 100%, so the baseline is 3. The description does not add parameter-level detail, but the schema already fully documents both personUrl and since with examples and semantics. Therefore, no deduction or bonus is warranted.

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 states a specific verb+resource: 'Retrieve a conversation with a LinkedIn person via standard LinkedIn messaging.' It clearly differentiates the tool's primary function from siblings like get_inbox or sync_inbox by focusing on a single conversation and explaining the sync workflow behavior.

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?

Provides explicit when-to-use and when-not-to-use guidance: if the conversation is already synced, it returns immediately; if not, it starts a sync workflow and instructs the agent to call get_workflow_result. It explicitly warns 'Do not retry the original tool while a workflow is still running; that creates duplicate queued work.' Also details how to handle pendingReason values like 'queued' and 'outsideWorkingHours.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/zecloud/remote-mcp-linkedin'

If you have feedback or need assistance with the MCP directory API, please join our Discord server