| name | vector-collection-management |
| skill_type | skill |
| description | Create, populate, list, and delete vector-store collections through the vector-mcp MCP server's vector_collection_management tool. Use when the agent must stand up a new RAG collection, ingest documents (from a directory, file paths/URLs, or raw text) into an existing collection, enumerate collections, or drop one — across any supported backend (chromadb, postgres/pgvector, qdrant, couchbase, mongodb). Do NOT use for running queries against a collection (use vector-hybrid-search) or for choosing/tuning a backend connection (vector-backend-operations). |
| license | MIT |
| tags | ["vector","rag","collections","embeddings","mcp"] |
| metadata | {"author":"Genius","version":"0.1.0"} |
Vector Collection Management
Lifecycle operations on vector-store collections — the named indexes that hold
embedded document chunks — via the vector-mcp MCP server. One condensed,
action-routed tool covers create / add / list / delete so the same call shape works
across every backend.
When to use
- Create (or get-or-create) a collection to hold a corpus for RAG.
- Ingest documents into a collection — from a
document_directory, a list of
document_paths (files/URLs), or inline document_contents strings.
- List the collections that exist on a backend.
- Delete a collection you no longer need.
When NOT to use
- Querying / retrieving from a collection →
vector-hybrid-search.
- Picking a backend, wiring host/port/credentials, or comparing engines →
vector-backend-operations.
- Persisting derived knowledge into the epistemic-graph KG → this is a vector
backend, not a KG source; use the graph-os tools for KG writes.
Prerequisites & environment
Connect via the mcp-client skill against the vector-mcp MCP server. Every
action takes the connection parameters inline (or falls back to server defaults):
| Parameter | Notes |
|---|
db_type | Backend: chromadb, postgres, qdrant, couchbase, mongodb |
db_path | On-disk path (e.g. for embedded chromadb) |
host / port | Networked backends |
db_name / username / password | Auth for postgres / couchbase / mongodb |
collection_name | Target collection (defaults to memory in the retriever) |
Embedding is handled server-side by the configured embedding model
(agent-utilities[embeddings-openai] or HuggingFace/sentence-transformers extras).
Tools & actions
| Condensed tool | Actions |
|---|
vector_collection_management | create_collection, add_documents, delete_collection, list_collections |
Key parameters
overwrite — on create_collection, replace an existing collection instead of
get-or-create.document_directory / document_paths / document_contents — the three
mutually-usable ingestion inputs for create_collection and add_documents.confirm — required guard on delete_collection.
Recipes
Create a collection and seed it from a directory:
{"action":"create_collection","db_type":"chromadb","db_path":"./chroma","collection_name":"handbook","document_directory":"./docs"}
Add inline text to an existing collection:
{"action":"add_documents","db_type":"chromadb","db_path":"./chroma","collection_name":"handbook","document_contents":["VPN setup: connect to gw-hq before syncing."]}
List collections on a backend:
{"action":"list_collections","db_type":"qdrant","host":"qdrant","port":"6333"}
Delete a collection (guarded):
{"action":"delete_collection","db_type":"chromadb","db_path":"./chroma","collection_name":"handbook","confirm":true}
Gotchas
create_collection is get-or-create by default; pass overwrite:true to rebuild
from scratch — this drops existing vectors.delete_collection needs confirm:true; without it the call is a no-op guard.- Ingest inputs are supported one-at-a-time per call; loaders derive from
llama-index-readers-file so PDFs/EPUBs/HTML are parsed, but very large
directories should be chunked into batches to avoid long-running calls.
- The backend must be reachable with the given
db_type/connection params or the
call errors before any write.
Related
vector-hybrid-search — retrieve from the collections you populate here.vector-backend-operations — backend selection and connection details.
