Qdrant MCP

Create a collection: a single unnamed vector (`vector_size` + `distance`), 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`), or a text collection (`text_model`) — exactly one of the three. `sparse_vectors` defines sparse (keyword-style) vectors at creation time. Text: `text_model` (a fastembed model, e.g. `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, 384 dims, multilingual, downloaded on first use) creates a `dense` Cosine vector of the model's size and records the model in the collection's metadata, so `text` in `qdrant_points_upsert` and `query_text` in `qdrant_query` embed with it. `text_language` (e.g. "spanish") adds a BM25 index on `text_field` (default `text`) for `bm25_query`: stemmed, accent-folded, stopwords removed where Qdrant has a list for the language (all but armenian and tamil); given alone, it makes a keyword-only collection with no vectors. The embedded local mode (QDRANT_LOCAL_PATH) can't rank such an index — BM25 there needs the default tokenization. `quantization_config` (scalar/product/binary) and `strict_mode_config` apply to the whole collection. `hnsw_config` and `optimizers_config` set the index and optimizer parameters from the start (they can also be changed later with `qdrant_collection_update`); `on_disk_payload=true` keeps payloads on disk instead of in RAM, which can only be chosen here. 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": {}}} Example (multitenant index, payload on disk): {"collection_name": "docs", "vector_size": 4, "hnsw_config": {"m": 0, "payload_m": 16}, "on_disk_payload": true} Example (text, semantic + BM25):…

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. `text_models` declares which fastembed model made each existing dense vector (`{"<vector name>": "<model>"}`, `""` for the unnamed vector), so `query_text` and `text` upserts work on a collection built elsewhere — it must be the model the stored vectors were made with, and its size must match the vector's. `null` removes a declaration (e.g. after deleting that vector). A collection with points won't switch a vector to a different model: remove the declaration first if it was wrong. `text_field` names the payload field holding the text (default `text`). Both are kept in the collection's metadata. 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. `with_payload` takes `true`/`false` or a list of keys to return only those (e.g. `["title"]`). Example: {"collection_name": "docs", "ids": [1, 2], "with_payload": ["title", "url"]}

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. `limit` is capped per call by the server's QDRANT_MCP_MAX_LIMIT (1000 by default). `with_payload` takes `true`/`false` or a list of keys to return only those. `order_by` sorts by a payload field instead of by id: a key, or `{"key": ..., "direction": "asc"|"desc", "start_from": value}`; the key needs a payload range index (integer, float or datetime). Paging works differently then: `offset` is rejected, and `next_page_offset` is always `null` even when more points remain — to continue, repeat the call with `start_from` set to the last value you received (points tied on that value are returned again, so de-duplicate by id). Example: {"collection_name": "docs", "limit": 50} Example (newest first): {"collection_name": "docs", "limit": 20, "order_by": {"key": "created_at", "direction": "desc"}}

Insert or replace points (id + vector or text + payload) in a collection. `vector` matches the collection's vector config: a plain list for a single unnamed vector; a list of lists for an unnamed multivector; or a dict keyed by vector name for named vectors, where each value is a dense list, a multivector, or a sparse vector (`{"indices": [...], "values": [...]}`). Give `text` instead of `vector` to have it embedded with every model the collection declares (see `qdrant_collection_create`'s `text_model`) and stored in its text field — one text is one point, nothing is split. A text longer than the model reads (128 tokens, ~500 characters, for the multilingual MiniLM) is refused rather than silently cut: split it into several points. Needs the `embeddings` extra. `update_mode` makes the write conditional: `insert_only` never overwrites an existing id, `update_only` never creates a new one (default `upsert` does both). `update_filter` only overwrites existing points whose current payload matches the filter; ids that don't exist yet are inserted regardless (verified). `wait=false` returns before the write is applied. 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"}} ]} Example (text): {"collection_name": "notes", "points": [ {"id": 1, "text": "Grants for young people's housing", "payload": {"year": 2026}} ]} Example (named dense + sparse): {"collection_name": "docs", "points": [ {"id": 1, "vector": {"dense": [0.1, 0.2, 0.3, 0.4], "sparse": {"indices": [1, 7], "values": [0.5, 0.2]}}} ]}

Search a collection with Qdrant's unified Query API: vector similarity, hybrid fusion, multi-stage reranking, MMR diversity, payload-based score formulas, ordering by a payload field, or random sampling. Exactly one query mode is required: - `query_vector` — nearest points to a dense vector, a multivector (list of vectors), a sparse vector (`{"indices": [...], "values": [...]}`), or a point id whose stored vector is reused (that point itself is left out of the results). It must match the vector selected with `using`. Add `mmr` (`diversity` 0..1, `candidates_limit`) to re-order the nearest candidates for diversity: set `candidates_limit` well above `limit` (it defaults to `limit`, which leaves MMR nothing to choose from). `offset` pages through the MMR ranking on Qdrant 1.20 (verified; older servers applied it inside the `limit` re-ranked results, so raise `limit` there instead). - `query_text` — semantic search with plain text: embedded with the model the collection declares for the vector in `using` (`using` can be omitted when it declares exactly one). Needs the `embeddings` extra; `mmr` works as with `query_vector`. - `bm25_query` — keyword search ranked by BM25 (Qdrant 1.20): `using` is a payload field whose `text` index has `scoring` set (defaults to the collection's declared text field). The index's own tokenizer, stemmer and stopwords apply. Needs no embeddings. - `fusion` + 2+ `prefetch` stages — combine retrieval strategies: `"rrf"` (Reciprocal Rank Fusion), `"dbsf"` (Distribution-Based Score Fusion), or `{"method": "rrf", "weights": [2, 1], "k": 60}` for RRF with one weight per stage (a count that doesn't match the stages is rejected by Qdrant). - `formula` — rescore the results of `prefetch` stages (required: a formula without prefetch is rejected). `{"expression": ..., "defaults": {...}}`. An expression is a number; a variable (`"$score"` is the prefetch score, any other string is a payload key); a payload…