google_search_images
Find and retrieve visual content from Google Images by specifying search queries, location filters, language preferences, and result parameters.
Instructions
Search Google for results
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | Yes | The query to search for | |
| gl | No | The country to search in, e.g. us, uk, ca, au, etc. | |
| location | No | The location to search in, e.g. San Francisco, CA, USA | |
| hl | No | The language to search in, e.g. en, es, fr, de, etc. | |
| page | No | The page number to return, first page is 1 (integer value as string) | 1 |
| tbs | No | The time period to search in, e.g. d, w, m, y | |
| num | No | The number of results to return, max is 100 (integer value as string) | 10 |
Implementation Reference
- src/serper_mcp_server/core.py:14-17 (handler)Handler function that executes the tool logic for 'google_search_images' by deriving the endpoint 'images' from the tool name and sending a POST request to the Serper Google Images API.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)
- Pydantic model defining the input schema for google_search_images tool, extending BaseRequest with additional fields tbs and num.class SearchRequest(BaseRequest): tbs: Optional[str] = Field( None, description="The time period to search in, e.g. d, w, m, y" ) num: str = Field( "10", pattern=r"^([1-9]|[1-9]\d|100)$", description="The number of results to return, max is 100 (integer value as string)", )
- src/serper_mcp_server/server.py:41-60 (registration)Registers the 'google_search_images' tool (among others) by generating Tool objects with name from SerperTools enum and inputSchema from the mapped Pydantic model.@server.list_tools() async def list_tools() -> List[Tool]: tools = [] for k, v in google_request_map.items(): tools.append( Tool( name=k.value, description="Search Google for results", inputSchema=v.model_json_schema(), ), ) tools.append(Tool( name=SerperTools.WEBPAGE_SCRAPE, description="Scrape webpage by url", inputSchema=WebpageRequest.model_json_schema(), )) return tools
- src/serper_mcp_server/server.py:25-38 (registration)Dictionary mapping SerperTools.GOOGLE_SEARCH_IMAGES to its input schema SearchRequest, used for validation and Tool registration.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, }
- src/serper_mcp_server/core.py:25-39 (helper)Utility function that performs the asynchronous HTTP POST to the Serper API endpoint, handling SSL, timeout, and JSON response.async def fetch_json(url: str, request: BaseModel) -> Dict[str, Any]: payload = request.model_dump(exclude_none=True) headers = { 'X-API-KEY': SERPER_API_KEY, 'Content-Type': 'application/json' } ssl_context = ssl.create_default_context(cafile=certifi.where()) connector = aiohttp.TCPConnector(ssl=ssl_context) timeout = aiohttp.ClientTimeout(total=AIOHTTP_TIMEOUT) async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session: async with session.post(url, headers=headers, json=payload) as response: response.raise_for_status() return await response.json()