Zilliz MCP Server

Conduct a vector similarity search with an optional scalar filtering expression. Args: cluster_id: ID of the cluster region_id: ID of the cloud region hosting the cluster endpoint: The cluster endpoint URL. Can be obtained by calling describe_cluster and using the connect_address field collection_name: The name of the collection to which this operation applies data: A list of vector embeddings. Zilliz Cloud searches for the most similar vector embeddings to the specified ones anns_field: The name of the vector field limit: The total number of entities to return (default: 10). The sum of this value and offset should be less than 100 db_name: The name of the database. Pass explicit dbName or leave empty when cluster is free or serverless filter: The filter used to find matches for the search offset: The number of records to skip in the search result. The sum of this value and limit should be less than 16,384 grouping_field: The name of the field that serves as the aggregation criteria output_fields: An array of fields to return along with the search results metric_type: The name of the metric type that applies to the current search (L2, IP, COSINE) search_params: Extra search parameters including radius and range_filter partition_names: The name of the partitions to which this operation applies consistency_level: The consistency level of the search operation (Strong, Eventually, Bounded) Returns: Dict containing the search results Example: { "code": 0, "data": [ { "color": "orange_6781", "distance": 1, "id": 448300048035776800 }, { "color": "red_4794", "distance": 0.9353201, "id": 448300048035776800 } ] }