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
            }
        ]
    }