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Scrapeless MCP Server

google-search

Retrieve Google search results programmatically using specified queries, language, and country parameters. Ideal for integrating web data into applications via the Scrapeless MCP Server.

Instructions

Fetch Google Search Results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
glNoParameter defines the country to use for the Google search. It's a two-letter country code. (e.g., us for the United States, uk for United Kingdom, or fr for France).
hlNoParameter defines the language to use for the Google search. It's a two-letter language code. (e.g., en for English, es for Spanish, or fr for French).
queryYesParameter defines the query you want to search. You can use anything that you would use in a regular Google search. e.g. inurl:, site:, intitle:. We also support advanced search query parameters such as as_dt and as_eq.

Implementation Reference

  • The asynchronous handle function that executes the Google search by calling client.deepserp.scrape with actor 'scraper.google.search' and maps the organic results to a simplified format.
    handle: async (params, client) => {
      return wrapMcpResponse(async () => {
        const data: any = await client.deepserp.scrape({
          actor: "scraper.google.search",
          input: params,
        });
        return (
          data?.organic_results?.map((i: any) => ({
            position: i.position,
            title: i.title,
            link: i.link,
            redirect_link: i.redirect_link,
            snippet: i.snippet,
            snippet_highlighted_words: i.snippet_highlighted_words,
            source: i.source,
          })) ?? []
        );
      });
    },
  • Input schema using Zod defining parameters q (search query), hl (language), gl (country) with descriptions and defaults.
    inputSchema: {
      q: z
        .string()
        .describe(
          "Parameter defines the query you want to search. You can use anything that you would use in a regular Google search. e.g. inurl:, site:, intitle:. We also support advanced search query parameters such as as_dt and as_eq."
        )
        .default("Top news headlines"),
      hl: z
        .string()
        .describe(
          "Parameter defines the language to use for the Google search. It's a two-letter language code. (e.g., en for English, es for Spanish, or fr for French)."
        )
        .default("en"),
      gl: z
        .string()
        .describe(
          "Parameter defines the country to use for the Google search. It's a two-letter country code. (e.g., us for the United States, uk for United Kingdom, or fr for France)."
        )
        .default("us"),
    },
  • src/server.ts:40-45 (registration)
    Registers all tools from the toolsList import (including the google_search tool) into the MCP server using server.tool.
    // tools registration
    Object.values(toolsList).forEach((tool) => {
      server.tool(tool.name, tool.description, tool.inputSchema, (params: any) =>
        tool.handle(params, getScrapelessClient())
      );
    });
  • src/tools/index.ts:1-1 (registration)
    Re-exports the googleSearch tool definition, including it in the toolsList used for MCP server registration.
    export * from "./deepserp/googleSearch.js";
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. 'Fetch' implies a read operation, but it doesn't disclose critical traits like rate limits, authentication needs, response format, pagination, or error handling. For a search tool with zero annotation coverage, this is a significant gap.

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 extremely concise with just three words, front-loaded and zero waste. Every word ('Fetch Google Search Results') directly contributes to stating the tool's purpose without unnecessary elaboration.

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 tool's complexity (search functionality with parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values, error cases, or behavioral constraints, leaving the agent with insufficient information to use the tool effectively beyond basic input.

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 schema already documents all three parameters (query, gl, hl) with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as examples or usage tips. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Fetch Google Search Results' states the basic action (fetch) and resource (Google Search Results), but it's vague about scope and format. It doesn't specify what kind of results (e.g., web pages, images, news) or how many results are returned. Without sibling tools, differentiation isn't needed, but the purpose could be more specific.

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. There are no sibling tools mentioned, so no explicit comparisons are needed, but it lacks context about use cases, prerequisites, or limitations. It's a generic statement with no usage instructions.

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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