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Serper MCP Server

by garylab

google_search_scholar

Search scholarly articles and academic literature using Google Scholar. Retrieve results with customizable country, language, location, and page settings.

Instructions

Search Google for results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesThe query to search for
glNoThe country to search in, e.g. us, uk, ca, au, etc.
locationNoThe location to search in, e.g. San Francisco, CA, USA
hlNoThe language to search in, e.g. en, es, fr, de, etc.
pageNoThe page number to return, first page is 1 (integer value as string)1
autocorrectNoAutomatically correct (boolean value as string: 'true' or 'false')true

Implementation Reference

  • The async function that executes the google_search_scholar tool. It splits the tool name ('google_search_scholar') on underscores, takes the last part ('scholar') as URI path, and sends a POST request to https://google.serper.dev/scholar.
    async def google(tool: SerperTools, request: BaseModel) -> Dict[str, Any]:
        uri_path = tool.value.split("_")[-1]
        url = f"https://google.serper.dev/{uri_path}"
        return await fetch_json(url, request)
  • Registration mapping: SerperTools.GOOGLE_SEARCH_SCHOLAR is mapped to AutocorrectRequest schema in the google_request_map, which is iterated over in list_tools() to register the tool with MCP.
    google_request_map = {
        SerperTools.GOOGLE_SEARCH: SearchRequest,
        SerperTools.GOOGLE_SEARCH_IMAGES: SearchRequest,
        SerperTools.GOOGLE_SEARCH_VIDEOS: SearchRequest,
        SerperTools.GOOGLE_SEARCH_PLACES: AutocorrectRequest,
        SerperTools.GOOGLE_SEARCH_MAPS: MapsRequest,
        SerperTools.GOOGLE_SEARCH_REVIEWS: ReviewsRequest,
        SerperTools.GOOGLE_SEARCH_NEWS: SearchRequest,
        SerperTools.GOOGLE_SEARCH_SHOPPING: ShoppingRequest,
        SerperTools.GOOGLE_SEARCH_LENS: LensRequest,
        SerperTools.GOOGLE_SEARCH_SCHOLAR: AutocorrectRequest,
        SerperTools.GOOGLE_SEARCH_PATENTS: PatentsRequest,
        SerperTools.GOOGLE_SEARCH_AUTOCOMPLETE: AutocorrectRequest,
    }
  • Input schema for google_search_scholar: AutocorrectRequest extends BaseRequest with fields q, gl, location, hl, page, and autocorrect.
    class AutocorrectRequest(BaseRequest):
        autocorrect: Optional[str] = Field(
            "true",
            pattern=r"^(true|false)$",
            description="Automatically correct (boolean value as string: 'true' or 'false')",
        )
  • Enum definition: SerperTools.GOOGLE_SEARCH_SCHOLAR = 'google_search_scholar' is the string identifier for the tool.
    GOOGLE_SEARCH_SCHOLAR = "google_search_scholar"
    GOOGLE_SEARCH_PATENTS = "google_search_patents"
    GOOGLE_SEARCH_AUTOCOMPLETE = "google_search_autocomplete"
    WEBPAGE_SCRAPE = "webpage_scrape"
Behavior2/5

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

With no annotations, the description must reveal behavioral traits. It only says 'Search Google' without clarifying that the tool targets scholarly content, nor does it mention pagination, rate limits, or result structure. This is insufficient for an agent to understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence, but it is under-specified rather than concise. It omits critical information about the tool's purpose and usage, making it ineffective.

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 6 parameters and no output schema, the description fails to provide sufficient context about the tool's return values, result format, or how it differs from siblings. It is incomplete for an agent to use effectively.

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 covers all 6 parameters with descriptions, so the description does not need to add meaning. However, it does not clarify how parameters interact (e.g., country vs. location). Baseline 3 is appropriate given high schema coverage.

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

Purpose2/5

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

The description 'Search Google for results' is vague and fails to distinguish this tool from sibling tools like 'google_search' which also searches Google. The name 'scholar' suggests academic content, but the description does not specify this, leading to ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives like 'google_search' or 'google_search_images'. The description lacks any context about the tool's specific domain (scholarly articles) or when it should be preferred.

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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