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Linked-API
by Linked-API

get_conversation

Retrieve LinkedIn message conversations with specific contacts by providing their profile URL. Optionally filter messages by date to access relevant communication history.

Instructions

Allows you to get a conversation with a LinkedIn person using standard LinkedIn messaging.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
personUrlYesThe LinkedIn URL of the person whose conversation you want to poll (e.g., 'https://www.linkedin.com/in/john-doe')
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.

Implementation Reference

  • Core handler function that executes the 'get_conversation' tool logic: polls for conversations, syncs if necessary, and returns the result.
    public override async execute({
      linkedapi,
      args: { personUrl, since },
    }: {
      linkedapi: LinkedApi;
      args: { personUrl: string; since?: string };
      workflowTimeout: number;
      progressToken?: string | number;
    }): Promise<TMappedResponse<TConversationPollResult>> {
      const conversations = await this.getConversation(linkedapi, personUrl, since);
      if (conversations.errors.length === 0) {
        return conversations;
      }
      const workflowId = await linkedapi.syncConversation.execute({ personUrl });
      await linkedapi.syncConversation.result(workflowId);
      return await this.getConversation(linkedapi, personUrl, since);
    }
  • Input schema definition for the 'get_conversation' tool, specifying parameters personUrl (required) and since (optional).
    inputSchema: {
      type: 'object',
      properties: {
        personUrl: {
          type: 'string',
          description:
            "The LinkedIn URL of the person whose conversation you want to poll (e.g., 'https://www.linkedin.com/in/john-doe')",
        },
        since: {
          type: 'string',
          description:
            "Optional 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.",
        },
      },
      required: ['personUrl'],
    },
  • Registration of the GetConversationTool instance in the LinkedApiTools constructor's tools array.
    new GetConversationTool(progressCallback),
  • Helper function that performs the actual conversation polling using linkedapi.pollConversations.
    private async getConversation(
      linkedapi: LinkedApi,
      personUrl: string,
      since?: string,
    ): Promise<TMappedResponse<TConversationPollResult>> {
      const conversations = await linkedapi.pollConversations([
        {
          personUrl: personUrl,
          type: 'st',
          since: since,
        },
      ]);
      return {
        data: conversations.data ? conversations.data[0] : undefined,
        errors: conversations.errors,
      };
    }
  • Import statement for GetConversationTool used in registration.
    import { GetConversationTool } from './tools/get-conversation.js';
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'allows you to get a conversation' but lacks details on permissions required, rate limits, whether it retrieves real-time or cached data, or how the conversation data is structured. This leaves significant gaps for a tool interacting with LinkedIn messaging.

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 a single, clear sentence that efficiently conveys the core purpose without unnecessary words. It is appropriately sized for a simple retrieval tool, though it could be slightly more structured by front-loading key details like the polling aspect implied by the tool name.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete for a tool that retrieves conversation data. It doesn't explain what 'get a conversation' returns (e.g., message list, metadata), how polling works, or error conditions, which are critical for an agent to use this tool effectively in a LinkedIn context.

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%, so the input schema fully documents both parameters (personUrl and since). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints, resulting in the baseline score of 3.

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 clearly states the action ('get a conversation') and target resource ('with a LinkedIn person'), using standard LinkedIn messaging as context. It distinguishes from siblings like 'send_message' or 'nv_send_message' by focusing on retrieval rather than sending, though it doesn't explicitly differentiate from 'nv_get_conversation' which appears to be a similar tool.

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

Usage Guidelines2/5

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

No explicit guidance on when to use this tool versus alternatives is provided. It mentions 'standard LinkedIn messaging' but doesn't clarify when to choose this over 'nv_get_conversation' or other messaging-related tools, nor does it specify prerequisites or exclusions for usage.

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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