Search, Query and interact with data in your Milvus Vector Database.
Milvus mcp server connects Milvus to AI assistants that speak the Model Context Protocol. Search, Query and interact with data in your Milvus Vector Database.
This repository contains a MCP server that provides access to Milvus vector database functionality.
Before using this MCP server, ensure you have: - Python 3.10 or higher - A running Milvus instance (local or remote) - uv installed (recommended for running the server)
Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.
Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| milvus_text_search | Search for documents using full text search |
| Parameters: | collection_name: Name of collection |
| query_text | Text query for similarity search |
| limit | The maximum number of results to return (default: 10) |
| output_fields | Fields to include in results |
| drop_ratio | Proportion of low-frequency terms to ignore (0.0-1.0) (default: 0.2) |
| milvus_vector_search | Perform vector similarity search on a collection |
| vector | Vector of the text query |
| vector_field | Field name for vector search |
| filter_expr | Filter expression to select entities to delete |
| metric_type | metric type, default to "COSINE"; for quick setup and will be ignored if field_schema is provided |
| radius | Optional lower bound for range search (default: None) |
| range_filter | Optional upper bound for range search (default: None) |
| milvus_hybrid_search | Perform hybrid search on a collection |
| text_field | Field name for text search |
| sparse_radius | Optional lower bound for sparse range search (default: None) |
| sparse_range_filter | Optional upper bound for sparse range search (default: None) |
| dense_radius | Optional lower bound for dense range search (default: None) |
| dense_range_filter | Optional upper bound for dense range search (default: None) |
| milvus_text_similarity_search | Perform text similarity search on a collection |
| anns_field | Field name for text search |
| milvus_query | Query collection using filter expressions |
| milvus_list_collections | List all collections in the database |
| milvus_create_collection | Create a new collection with quick setup or customized schema |
| auto_id | whether to auto generate id, default to True |
| dimension | vector dimension, default to 768; for quick setup and will be ignored if field_schema is provided |
| primary_field_name | name of the primary field, default to "id"; for quick setup and will be ignored if field_schema is provided |
| vector_field_name | name of the vector field, default to "vector"; for quick setup and will be ignored if field_schema is provided |
| field_schema | List of field schema, each element is a dictionary with the following keys: |
| index_params | Optional list of index parameters, each element is a dictionary with the following keys: |
| field_name | name of the field to index |
| index_type | index type |
| other_kwargs | Additional keyword arguments for the collection creation |
| milvus_load_collection | Load a collection into memory for search and query |
| replica_number | Number of replicas (default: 1) |
| milvus_release_collection | Release a collection from memory |
| milvus_get_collection_info | Lists detailed information like schema, properties, collection ID, and other metadata of a specific collection. |
| milvus_insert_data | Insert data into a collection |
| data | Dictionary mapping field names to lists of values |
| milvus_delete_entities | Delete entities from a collection based on filter expression |
{
"mcpServers": {
"milvus-sse": {
"url": "http://your_sse_host:port/sse",
"disabled": false,
"autoApprove": []
}
}
}From the project documentation. Replace placeholders and example paths before connecting.
Inspect PostgreSQL schemas and run read-only queries with the archived MCP reference server. Review its maintenance status before choosing it.
Connect the Notion MCP server to search workspace content and work with pages and databases through Notion’s hosted OAuth service.
Use the Supabase MCP server to inspect database schemas, query project data and retrieve docs through a project-scoped connection.
Query, modify and analyse local SQLite databases in conversation — the fastest way to chat with a data file.
Metabase ships its own MCP endpoint — search your BI content, build and run queries, and save questions and dashboards without leaving the chat.
Official MongoDB server covering data, schemas and Atlas management — from find queries to spinning up clusters.