Qdrant MCP

Create a collection: either a single unnamed vector (`vector_size` + `distance`), or one or more named vectors (`vectors`, each a full `VectorParams` — size, distance, and optionally its own `multivector_config` for ColBERT-style multi-vectors or `quantization_config`) — exactly one of the two. `sparse_vectors` defines sparse (keyword-style) vectors at creation time. `quantization_config` (scalar/product/binary) and `strict_mode_config` apply to the whole collection. Fails with a clear error if a collection with this name already exists. Example (simple): {"collection_name": "docs", "vector_size": 4, "distance": "Cosine"} Example (hybrid): {"collection_name": "docs", "vectors": { "dense": {"size": 4, "distance": "Cosine"} }, "sparse_vectors": {"sparse": {}}}

Delete a collection and all its points; a no-op if it doesn't exist. Example: {"collection_name": "docs"}

Check whether a collection exists, without raising if it doesn't. Example: {"collection_name": "docs"}

Return full config and status of one collection. Fails with a clear error if the collection doesn't exist. Example: {"collection_name": "docs"}

List every collection name in the configured Qdrant instance. Example: {}

Update optimizer/HNSW/collection/vector params on an existing collection. Only the fields you pass are changed; omitted ones keep their current value. `quantization_config="disabled"` turns quantization off. `vectors_config`/`sparse_vectors_config` only **adjust** named vectors that already exist (HNSW/quantization/index tuning) — they cannot add a new one; use `qdrant_collection_vector_create` for that, or this fails with Qdrant's own "Not existing vector name" error. Fails with a clear error if the collection doesn't exist. Example: {"collection_name": "docs", "optimizers_config": {"indexing_threshold": 10000}}

Confirm the configured Qdrant instance is reachable and responding. Never raises to the caller: a health check that raises on the exact condition it exists to detect defeats its own purpose. Connection failures are logged and reported in the result's ok/error fields instead, so a client renders them without a tool-call error round-trip.

Count points in a collection, optionally matching a filter. Example: {"collection_name": "docs"}

Delete points by id list or by payload filter — exactly one of the two. Deleting an id that doesn't exist is not an error (Qdrant treats it as a no-op); this only fails if the collection itself is missing, or if you provide zero or both selectors. Example (by id): {"collection_name": "docs", "ids": [1, 2]} Example (by filter): {"collection_name": "docs", "points_filter": { "must": [{"key": "city", "match": {"value": "ny"}}] }}

Retrieve points by id; unknown ids are simply omitted, not an error. Fails only if the collection itself doesn't exist. Example: {"collection_name": "docs", "ids": [1, 2]}

Page through all points in a collection, optionally filtered. Pass the returned `next_page_offset` as `offset` to fetch the next page; `null` means there are no more pages. Example: {"collection_name": "docs", "limit": 50}

Insert or replace points (id + vector + payload) in a collection. Fails with a clear error if the collection doesn't exist. Example: {"collection_name": "docs", "points": [ {"id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "payload": {"city": "ny"}} ]}

Vector similarity search, with optional hybrid search over multiple prefetch stages. Pass `query_vector` (a literal vector, or a point id to reuse an existing point's vector) for a plain nearest-vector query, or `fusion` + 2+ `prefetch` stages to combine multiple retrieval strategies via Reciprocal Rank Fusion (`fusion="rrf"`) or Distribution-Based Score Fusion (`fusion="dbsf"`) — exactly one of `query_vector`/`fusion` is required. `using` selects a named vector; `lookup_from` resolves `query_vector` from a point id in another collection instead of the current one. Fails with a clear error if the collection doesn't exist. Example (plain): {"collection_name": "docs", "query_vector": [0.1, 0.2, 0.3, 0.4], "limit": 5} Example (hybrid): {"collection_name": "docs", "fusion": "rrf", "prefetch": [ {"query_vector": [0.1, 0.2, 0.3, 0.4], "using": "dense", "limit": 20}, {"query_vector": [0.5, 0.5], "using": "sparse", "limit": 20} ], "limit": 5}