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automcp-app

Linkd MCP Server

by automcp-app

search_for_companies

Find companies on LinkedIn using search queries and filters like match thresholds to identify relevant business entities for research or networking purposes.

Instructions

Search for companies on Linkd using filters like query and match threshold.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query, e.g., 'Tech companies in California'
limitNoMaximum number of results to return (1–30)
acceptance_thresholdNoMatch score threshold between 0 and 100

Implementation Reference

  • The main handler function for the 'search_for_companies' tool. It constructs a search URL with query, limit, and threshold parameters, makes a request to the Linkd API using makeLinkdRequest, handles errors, and returns the response in MCP content format.
    export const searchForCompaniesTool = async ({
      query,
      limit = 10,
      acceptance_threshold = 60,
    }: SearchForCompaniesParams) => {
      const url = new URL("https://search.linkd.inc/api/search/companies");
      url.searchParams.append("query", query);
      url.searchParams.append("limit", String(Math.min(limit, 30)));
      url.searchParams.append("acceptance_threshold", String(Math.max(0, Math.min(100, acceptance_threshold))));
    
      const response = await makeLinkdRequest(url.toString(), {});
      const responseData = await response.json();
    
      if (responseData.error) {
        throw new Error(
          `Failed to search for companies: ${JSON.stringify(responseData.error)}`
        );
      }
    
      return {
        content: [
          {
            type: "text" as const,
            text: `search completed successfully: ${JSON.stringify(responseData, null, 2)}`
          }
        ]
      };
    };
  • Zod schema defining the input parameters for the tool: query (required string), limit (optional number 1-30, default 10), acceptance_threshold (optional number 0-100, default 60).
    export const searchForCompaniesSchema = {
      query: z.string().describe("The search query, e.g., 'Tech companies in California'"),
      limit: z.number().min(1).max(30).default(10).describe("Maximum number of results to return (1–30)"),
      acceptance_threshold: z
        .number()
        .min(0)
        .max(100)
        .default(60)
        .describe("Match score threshold between 0 and 100"),
    };
  • Registers the 'search_for_companies' tool on the MCP server by calling server.tool with the name, description, schema, and handler function.
    server.tool(
      searchForCompaniesName,
      searchForCompaniesDescription,
      searchForCompaniesSchema,
      searchForCompaniesTool
    );
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 mentions 'filters like query and match threshold' which hints at search functionality, but doesn't describe what the search returns, pagination behavior, rate limits, authentication requirements, or error conditions. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 states the core purpose upfront. Every word serves a purpose - 'Search for companies on Linkd' establishes the action and target, while 'using filters like query and match threshold' adds useful operational context without redundancy.

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?

For a search tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what kind of results are returned (company profiles, basic info, etc.), how results are ordered, whether there's pagination, or what happens when no matches are found. The agent would need to guess about the tool's behavior and outputs.

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 description mentions 'filters like query and match threshold' which maps to two of the three parameters (query and acceptance_threshold). However, with 100% schema description coverage, the schema already fully documents all parameters including 'limit'. The description adds minimal value beyond what's in the schema, meeting the baseline for high coverage.

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 'Search' and resource 'companies on Linkd', making the purpose immediately understandable. It also mentions 'filters like query and match threshold' which provides additional context about how the search works. However, it doesn't explicitly differentiate this from sibling tools like 'search_for_users' or 'scrape_linkedin', which would require a 5.

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 like 'search_for_users' or 'scrape_linkedin'. It mentions filters but doesn't specify use cases, prerequisites, or exclusions. Without any contextual direction, the agent must infer usage from the tool name alone.

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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