{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-milvus","slug":"milvus","name":"Milvus","type":"mcp","url":"https://github.com/zilliztech/mcp-server-milvus","page_url":"https://unfragile.ai/milvus","categories":["mcp-servers","rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-milvus__cap_0","uri":"capability://search.retrieval.vector.similarity.search.with.mcp.protocol","name":"vector-similarity-search-with-mcp-protocol","description":"Executes semantic similarity searches against Milvus vector database collections using the Model Context Protocol (MCP) transport layer. Converts natural language or embedding queries into vector search operations through MCP tool definitions, handling distance metric selection (L2, IP, cosine) and result ranking. The MCP server translates search requests into native Milvus SDK calls, managing connection pooling and result serialization back to the client.","intents":["Search for semantically similar documents or embeddings without writing SQL","Find top-K nearest neighbors in a vector collection with configurable distance metrics","Integrate vector search into LLM agent workflows via standardized MCP tool interface","Query embeddings generated by external models (OpenAI, Hugging Face) against stored vectors"],"best_for":["LLM application developers building RAG systems with Claude or other MCP-compatible clients","Teams integrating vector search into multi-step agent workflows","Developers prototyping semantic search without managing Milvus SDK directly"],"limitations":["Search latency depends on Milvus collection size and index type; unindexed collections perform brute-force scanning","MCP protocol adds serialization overhead (~10-50ms per request) compared to direct SDK calls","No built-in result caching — repeated identical queries hit the database each time","Limited to single-collection searches per request; cross-collection joins require multiple calls"],"requires":["Milvus 2.3.0 or higher with running instance (local or remote)","MCP-compatible client (Claude desktop, custom MCP host, or compatible LLM framework)","Pre-populated Milvus collection with vector embeddings","Network connectivity to Milvus server (TCP/gRPC)"],"input_types":["vector embeddings (float32 arrays, 1-4096 dimensions typical)","query parameters (top_k integer, distance threshold float)","collection name (string identifier)"],"output_types":["ranked result sets with entity IDs and distances","structured JSON containing matched records and similarity scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_1","uri":"capability://data.processing.analysis.structured.data.query.and.filtering","name":"structured-data-query-and-filtering","description":"Executes filtered queries against Milvus collections using scalar field predicates (equality, range, text matching) combined with optional vector search. The MCP server translates filter expressions into Milvus query DSL, supporting WHERE clauses on metadata fields (integers, strings, booleans) alongside vector similarity. Results are ranked by vector distance when applicable, with scalar filters applied before or after vector search depending on index configuration.","intents":["Find vectors matching specific metadata criteria (e.g., 'documents from 2024 with author=Alice')","Combine semantic search with business logic filters (category, date range, status)","Query hybrid search scenarios where both vector similarity and scalar attributes matter","Retrieve full entity records (vectors + metadata) matching complex filter conditions"],"best_for":["E-commerce and content platforms filtering search results by category, price, or date","Document management systems combining semantic search with access control metadata","Multi-tenant applications isolating results by tenant_id or organization_id"],"limitations":["Filter performance degrades with high cardinality scalar fields or complex boolean expressions","No support for cross-field joins or aggregations (GROUP BY, COUNT) — requires post-processing","Scalar index types (hash, inverted) have different performance characteristics; wrong index choice causes full collection scans","Filter syntax is Milvus-specific; porting queries to other databases requires translation"],"requires":["Milvus 2.3.0+ with scalar fields defined in collection schema","Appropriate scalar indexes created on frequently-filtered fields","MCP client capable of passing complex filter expressions as tool parameters"],"input_types":["filter expressions (Milvus query DSL strings, e.g., 'status == \"active\" AND created_at > 1704067200')","collection name and field names (strings)","optional vector query parameters (top_k, distance threshold)"],"output_types":["filtered entity records with all fields (vectors and scalars)","result count and pagination metadata"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_2","uri":"capability://data.processing.analysis.collection.schema.inspection.and.metadata.discovery","name":"collection-schema-inspection-and-metadata-discovery","description":"Introspects Milvus collection schemas to expose field definitions, vector dimensions, index types, and partition information through MCP tools. The server queries Milvus system metadata (via describe_collection and list_indexes APIs) and returns structured schema information, enabling clients to understand collection structure without manual documentation. Supports listing all collections, examining field types (vector, scalar), and retrieving index configuration details.","intents":["Discover available collections and their schemas programmatically before querying","Determine vector dimensionality to validate embedding inputs","Inspect index types and parameters to understand query performance characteristics","Validate that required fields exist before constructing filter expressions"],"best_for":["Dynamic applications that adapt queries based on discovered schema","Multi-collection systems where schema varies across collections","Development and debugging workflows requiring schema visibility"],"limitations":["Schema inspection is read-only; no schema modification capabilities (add/drop fields, reindex)","Returns point-in-time snapshot; schema changes in Milvus are not reflected until next inspection call","No schema versioning or change history — cannot track schema evolution over time"],"requires":["Milvus 2.3.0+ with accessible system metadata","Read permissions on target collections"],"input_types":["collection name (optional; if omitted, lists all collections)","field name (optional; for detailed field inspection)"],"output_types":["collection metadata (name, row count, creation time)","field definitions (name, data type, dimension for vectors)","index information (index type, metric type, parameters)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_3","uri":"capability://data.processing.analysis.batch.vector.insertion.and.upsert","name":"batch-vector-insertion-and-upsert","description":"Inserts or updates multiple vectors and associated scalar metadata into Milvus collections in a single operation. The MCP server batches insert/upsert requests, handling primary key management, timestamp assignment, and partition routing. Supports both insert (append-only) and upsert (insert-or-update) semantics, with automatic ID generation or user-provided IDs. Returns insertion statistics (inserted count, failed count) and generated IDs for tracking.","intents":["Bulk load embeddings from external sources (APIs, files) into Milvus","Update existing vectors when embeddings are re-computed or refined","Maintain consistency between vector and metadata fields during inserts","Batch multiple embedding operations to reduce network round-trips"],"best_for":["Data pipeline teams ingesting embeddings from embedding services (OpenAI, Hugging Face)","RAG systems refreshing document embeddings periodically","Applications requiring atomic multi-field updates (vector + metadata together)"],"limitations":["Batch size limits vary by Milvus configuration; very large batches (>1M vectors) may require chunking","No transactional guarantees across multiple batches; partial failures leave inconsistent state","Upsert performance is slower than insert-only due to primary key lookups","Inserted data is not immediately queryable if collection has no index; index refresh may be required"],"requires":["Milvus 2.3.0+ with write permissions on target collection","Pre-defined collection schema matching vector dimensions and scalar field types","Vectors as float32 arrays matching collection dimensionality"],"input_types":["vector embeddings (float32 arrays, matching collection dimension)","scalar metadata fields (strings, integers, booleans per schema)","optional primary keys (integers or strings; auto-generated if omitted)","optional partition name for partition-based routing"],"output_types":["insertion statistics (inserted_count, upserted_count, failed_count)","generated primary keys (if auto-generated)","error details for failed records"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_4","uri":"capability://automation.workflow.collection.lifecycle.management","name":"collection-lifecycle-management","description":"Creates, drops, and manages Milvus collections through MCP tools. Supports collection creation with custom schema definition (vector fields, scalar fields, primary keys), deletion of collections, and collection state inspection (loaded, unloaded). The server translates MCP parameters into Milvus collection operations, handling schema validation and resource allocation. Enables dynamic collection provisioning without direct Milvus CLI access.","intents":["Programmatically create collections for new data domains or tenants","Clean up test or temporary collections after experiments","Manage collection lifecycle in multi-tenant or multi-project systems","Automate collection provisioning as part of application deployment"],"best_for":["Multi-tenant SaaS platforms creating per-tenant collections dynamically","Development teams automating test environment setup","Applications with ephemeral data requiring collection creation/deletion"],"limitations":["Collection creation is synchronous and may block for large schema definitions; no async provisioning","Dropping collections is destructive and irreversible; no soft-delete or recovery mechanism","Schema changes (add/drop fields) are not supported; requires collection recreation","No collection cloning or templating; each collection must be created from scratch"],"requires":["Milvus 2.3.0+ with admin/write permissions","Schema definition matching Milvus collection schema format (field names, types, dimensions)"],"input_types":["collection name (string, must be unique within Milvus instance)","schema definition (field definitions with types, dimensions, primary key designation)","optional consistency level (strong, bounded, session, eventual)"],"output_types":["collection creation confirmation (collection_id, creation_time)","collection deletion confirmation","collection state (loaded, unloaded, dropped)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_5","uri":"capability://automation.workflow.index.creation.and.optimization","name":"index-creation-and-optimization","description":"Creates and configures vector and scalar indexes on Milvus collections to optimize query performance. The MCP server exposes index creation tools supporting multiple index types (IVF_FLAT, HNSW, SCANN for vectors; hash, inverted for scalars) with tunable parameters (nlist, M, ef_construction). Handles index building asynchronously and provides index status inspection. Enables performance tuning without direct Milvus configuration.","intents":["Create vector indexes to accelerate similarity search on large collections","Build scalar indexes on frequently-filtered metadata fields","Tune index parameters (nlist, M) to balance search speed vs memory usage","Monitor index build progress and status"],"best_for":["Performance-critical RAG systems requiring sub-100ms query latency","Large-scale collections (>1M vectors) where unindexed search is prohibitively slow","Teams optimizing query performance iteratively"],"limitations":["Index building is resource-intensive and may cause temporary query slowdowns on large collections","Index parameters are collection-specific; no automatic tuning — requires manual experimentation","Changing index type requires dropping and rebuilding, causing downtime","Index memory overhead can be 10-50% of raw vector size depending on index type"],"requires":["Milvus 2.3.0+ with sufficient disk/memory for index storage","Collection populated with vectors before index creation","Understanding of index types and parameter trade-offs (speed vs memory vs accuracy)"],"input_types":["field name (vector or scalar field to index)","index type (IVF_FLAT, HNSW, SCANN, hash, inverted)","index parameters (nlist, M, ef_construction, etc. depending on type)","metric type for vector indexes (L2, IP, cosine)"],"output_types":["index creation confirmation (index_name, field_name, index_type)","index build status (building, finished, failed)","index statistics (indexed_rows, build_time)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_6","uri":"capability://data.processing.analysis.entity.deletion.and.purging","name":"entity-deletion-and-purging","description":"Deletes individual entities or batches of entities from Milvus collections by primary key or filter expression. The MCP server translates deletion requests into Milvus delete operations, supporting both targeted deletion (by ID) and bulk deletion (by filter). Handles soft deletes via filter expressions and hard deletes via primary key. Returns deletion statistics (deleted_count, failed_count).","intents":["Remove outdated or incorrect embeddings from collections","Implement data retention policies (delete records older than N days)","Support user-initiated data deletion (GDPR, privacy compliance)","Clean up duplicate or corrupted records"],"best_for":["Privacy-conscious applications requiring data deletion capabilities","Systems with evolving data where old embeddings must be purged","Multi-tenant platforms deleting tenant data on account closure"],"limitations":["Deletion by filter expression is slower than deletion by primary key; full collection scan may be required","Deleted space is not immediately reclaimed; requires collection compaction (separate operation)","No soft-delete mechanism; deletions are permanent and irreversible","Bulk deletions may cause temporary query performance degradation"],"requires":["Milvus 2.3.0+ with write permissions on target collection","Primary keys or filter expressions identifying entities to delete"],"input_types":["primary keys (list of integers or strings for targeted deletion)","filter expression (Milvus query DSL for bulk deletion by criteria)"],"output_types":["deletion statistics (deleted_count, failed_count)","error details for failed deletions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-milvus__cap_7","uri":"capability://tool.use.integration.mcp.tool.schema.definition.and.validation","name":"mcp-tool-schema-definition-and-validation","description":"Defines and validates MCP tool schemas that map to Milvus operations, ensuring type safety and parameter validation before execution. The MCP server implements JSON Schema definitions for each tool (search, insert, delete, etc.), validating incoming requests against schema constraints (required fields, type matching, value ranges). Provides clear error messages for schema violations, preventing malformed Milvus operations.","intents":["Ensure type-safe tool invocations from LLM clients","Provide schema documentation for LLM agents to understand tool parameters","Catch parameter errors early before hitting Milvus","Enable IDE autocomplete and validation in MCP-aware clients"],"best_for":["LLM agent developers requiring strict parameter validation","Multi-user systems where schema enforcement prevents accidental misuse","Development environments where early error detection is valuable"],"limitations":["Schema validation adds ~5-10ms latency per request","Complex nested schemas may be difficult to express in JSON Schema","No runtime type coercion; type mismatches cause immediate rejection"],"requires":["MCP-compatible client that respects tool schemas","JSON Schema understanding in client implementation"],"input_types":["tool invocation parameters (any type matching schema definition)"],"output_types":["validation success/failure with detailed error messages","schema documentation (JSON Schema format)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Milvus 2.3.0 or higher with running instance (local or remote)","MCP-compatible client (Claude desktop, custom MCP host, or compatible LLM framework)","Pre-populated Milvus collection with vector embeddings","Network connectivity to Milvus server (TCP/gRPC)","Milvus 2.3.0+ with scalar fields defined in collection schema","Appropriate scalar indexes created on frequently-filtered fields","MCP client capable of passing complex filter expressions as tool parameters","Milvus 2.3.0+ with accessible system metadata","Read permissions on target collections","Milvus 2.3.0+ with write permissions on target collection"],"failure_modes":["Search latency depends on Milvus collection size and index type; unindexed collections perform brute-force scanning","MCP protocol adds serialization overhead (~10-50ms per request) compared to direct SDK calls","No built-in result caching — repeated identical queries hit the database each time","Limited to single-collection searches per request; cross-collection joins require multiple calls","Filter performance degrades with high cardinality scalar fields or complex boolean expressions","No support for cross-field joins or aggregations (GROUP BY, COUNT) — requires post-processing","Scalar index types (hash, inverted) have different performance characteristics; wrong index choice causes full collection scans","Filter syntax is Milvus-specific; porting queries to other databases requires translation","Schema inspection is read-only; no schema modification capabilities (add/drop fields, reindex)","Returns point-in-time snapshot; schema changes in Milvus are not reflected until next inspection call","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=milvus","compare_url":"https://unfragile.ai/compare?artifact=milvus"}},"signature":"jyFz+1R6YWE1u3uPBuVHhwMgOlBYHhRsj3TZ1XBRUmuKRGn16TQND6yz6S/qt55rYu5WDFnKMOaZLneAkplHAQ==","signedAt":"2026-06-19T21:01:15.590Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/milvus","artifact":"https://unfragile.ai/milvus","verify":"https://unfragile.ai/api/v1/verify?slug=milvus","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}