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automcp-app

Linkd MCP Server

by automcp-app

check_deep_research_status

Monitor the progress of a LinkedIn deep research job by providing its ID to track completion status and results.

Instructions

Check the status of an ongoing deep research job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job ID to check status for

Implementation Reference

  • The main handler function that executes the tool logic: constructs a URL for the deep research status API, makes a request using makeLinkdRequest, handles errors, and returns the status as text content.
    export const checkDeepResearchStatusTool = async ({
      job_id,
    }: CheckDeepResearchStatusParams) => {
      const statusUrl = new URL(`https://search.linkd.inc/api/search/deep_research/${job_id}`);
      const response = await makeLinkdRequest(statusUrl.toString(), {});
      const responseData = await response.json();
    
      if (responseData.error) {
        throw new Error(
          `Failed to check deep research status: ${JSON.stringify(responseData.error)}`
        );
      }
    
      return {
        content: [
          {
            type: "text" as const,
            text: `deep research status: ${JSON.stringify(responseData, null, 2)}`
          }
        ]
      };
    };
  • Zod schema defining the input parameters: job_id as string with description.
    export const checkDeepResearchStatusSchema = {
      job_id: z.string().describe("The job ID to check status for"),
    };
  • Registration of the tool in the MCP server using server.tool() with name, description, schema, and handler.
    server.tool(
      checkDeepResearchStatusName,
      checkDeepResearchStatusDescription,
      checkDeepResearchStatusSchema,
      checkDeepResearchStatusTool
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this checks status but doesn't explain what statuses exist (e.g., pending, running, completed), whether it's idempotent, if there are rate limits, or what the response format looks like. This leaves significant gaps for a tool that likely returns progress or result information.

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 front-loads the core purpose without any wasted words. It's appropriately sized for a simple status-checking tool and earns its place by clearly stating the action and target.

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 the complexity of checking job status (which often involves progress indicators, error states, or result readiness) and the lack of both annotations and an output schema, the description is incomplete. It doesn't explain what information is returned, leaving the agent uncertain about how to interpret the status or what to do next after checking.

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?

The schema description coverage is 100%, with the single parameter 'job_id' clearly documented in the schema. The description doesn't add any meaning beyond this (e.g., where to get the job_id from or format examples), so it meets the baseline of 3 where the 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 verb ('Check') and resource ('status of an ongoing deep research job'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'initiate_deep_research' or 'retrieve_contacts' beyond the 'check status' action, which prevents a perfect score.

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 prerequisites (e.g., that a job must be initiated first), exclusions, or relationships with sibling tools like 'initiate_deep_research' for starting jobs or 'retrieve_contacts' for getting results.

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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