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Linked-API
by Linked-API

nv_get_conversation

Retrieve LinkedIn Sales Navigator conversation history with a specific person using their profile URL. Optionally filter messages by date to focus on recent interactions.

Instructions

Allows you to get a conversation with a LinkedIn person using Sales Navigator 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

  • The overridden execute method that implements the core logic of the 'nv_get_conversation' tool. It polls conversations for the given person URL, syncs the conversation if there are errors, and returns the mapped response.
    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.nvSyncConversation.execute({ personUrl });
      await linkedapi.nvSyncConversation.result(workflowId);
      return await this.getConversation(linkedapi, personUrl, since);
    }
  • Zod schema defining the input parameters for the tool: personUrl (required string) and since (optional string). Used for input validation.
    protected readonly schema = z.object({
      personUrl: z.string(),
      since: z.string().optional(),
    });
  • The getTool method returns the MCP Tool definition, including name, description, and detailed inputSchema for the 'nv_get_conversation' tool.
    public override getTool(): Tool {
      return {
        name: this.name,
        description:
          'Allows you to get a conversation with a LinkedIn person using Sales Navigator messaging.',
        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'],
        },
      };
    }
  • Private helper method that polls conversations using the LinkedApi pollConversations method for Sales Navigator (type 'nv') and maps the response.
    private async getConversation(
      linkedapi: LinkedApi,
      personUrl: string,
      since?: string,
    ): Promise<TMappedResponse<TConversationPollResult>> {
      const conversations = await linkedapi.pollConversations([
        {
          personUrl: personUrl,
          type: 'nv',
          since: since,
        },
      ]);
      return {
        data: conversations.data ? conversations.data[0] : undefined,
        errors: conversations.errors,
      };
    }
  • The NvGetConversationTool is instantiated and added to the array of available tools in the LinkedApiTools class constructor.
    new NvSearchCompaniesTool(progressCallback),
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool 'allows you to get a conversation' but doesn't clarify if this is a read-only operation, what permissions are needed, whether it polls or retrieves real-time data, or any rate limits. This leaves significant behavioral aspects undocumented.

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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded with the core purpose, making it easy to parse quickly.

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

Completeness3/5

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

For a tool with 2 parameters, 100% schema coverage, and no output schema, the description is minimally adequate. It states what the tool does but lacks behavioral context (especially given no annotations) and usage guidance. The absence of output schema means the description doesn't need to explain return values, but overall completeness is limited.

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. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain format requirements for personUrl beyond 'LinkedIn URL' or provide examples for 'since'). Baseline 3 is appropriate when schema does the heavy lifting.

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 the resource ('with a LinkedIn person using Sales Navigator messaging'), making the purpose understandable. It distinguishes from generic 'get_conversation' by specifying 'Sales Navigator messaging' context, though it doesn't explicitly differentiate from all siblings like 'send_message' or 'nv_send_message'.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to choose this over 'get_conversation' (a sibling tool) or other messaging-related tools like 'send_message', 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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