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automcp-app

Linkd MCP Server

by automcp-app

scrape_linkedin

Extract LinkedIn profile data and posts with comments from any profile URL to gather professional insights and content analysis.

Instructions

Retrieves detailed profile data and posts with comments from a LinkedIn profile URL using RapidAPI. Each request costs 2 credits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkedin_urlYesThe LinkedIn profile URL to scrape.

Implementation Reference

  • The main execution logic for the 'scrape_linkedin' tool, which scrapes LinkedIn profile data via RapidAPI.
    export const scrapeLinkedinTool = async ({
      linkedin_url,
    }: ScrapeLinkedinParams) => {
      const apiUrl = new URL("https://search.linkd.inc/api/enrich/scrape");
      apiUrl.searchParams.append("linkedin_url", linkedin_url);
    
      const response = await makeLinkdRequest(apiUrl.toString(), {});
      const responseData = await response.json();
    
      if (responseData.error) {
        throw new Error(
          `Failed to scrape LinkedIn profile: ${JSON.stringify(responseData.error)}`
        );
      }
    
      return {
        content: [
          {
            type: "text" as const,
            text: `profile scraping completed successfully: ${JSON.stringify(responseData, null, 2)}`
          }
        ]
      };
    };
  • Zod schema and TypeScript type for input parameters of the scrape_linkedin tool.
    export const scrapeLinkedinSchema = {
      linkedin_url: z.string().describe("The LinkedIn profile URL to scrape."),
    };
    
    type ScrapeLinkedinParams = {
      linkedin_url: string;
    };
  • Registration of the 'scrape_linkedin' tool on the MCP server.
    server.tool(
      scrapeLinkedinName,
      scrapeLinkedinDescription,
      scrapeLinkedinSchema,
      scrapeLinkedinTool
    );
  • Name and description constants used for tool registration.
    export const scrapeLinkedinName = "scrape_linkedin";
    
    export const scrapeLinkedinDescription = "Retrieves detailed profile data and posts with comments from a LinkedIn profile URL using RapidAPI. Each request costs 2 credits.";
Behavior4/5

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 effectively adds value by specifying the data source (LinkedIn via RapidAPI) and cost implications ('Each request costs 2 credits'), which are critical behavioral traits not covered by the input schema. It doesn't describe output format or error handling, but for a tool with no annotations, this is a strong effort.

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 highly concise and well-structured in a single sentence that front-loads the core functionality ('Retrieves detailed profile data and posts with comments') and efficiently adds operational context ('using RapidAPI') and cost ('Each request costs 2 credits'). Every part earns its place without redundancy or waste.

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?

Given the tool's complexity (scraping with external API), no annotations, and no output schema, the description is moderately complete. It covers purpose, source, and cost, but lacks details on output format, error cases, rate limits, or authentication needs. For a tool with these gaps, it's adequate but has clear room for improvement.

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 input schema has 100% description coverage, with the single parameter 'linkedin_url' fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as URL format examples or validation rules. According to the rules, with high schema coverage, the baseline is 3, which is appropriate here.

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 tool's purpose: 'Retrieves detailed profile data and posts with comments from a LinkedIn profile URL.' It specifies the verb (retrieves), resource (profile data and posts with comments), and source (LinkedIn profile URL). However, it doesn't explicitly differentiate from sibling tools like 'enrich_linkedin' or 'search_for_users,' 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 Guidelines3/5

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

The description implies usage context by mentioning 'using RapidAPI' and cost ('Each request costs 2 credits'), which suggests when to consider this tool for scraping. However, it lacks explicit guidance on when to use this versus alternatives like 'enrich_linkedin' or 'search_for_users,' and doesn't state prerequisites or exclusions, leaving usage somewhat ambiguous.

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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