Vector Database

Capabilities

• • Store documents as chunks with a metadata tag on each
• • Compute embeddings with the model of the configuration
• • Search by cosine distance between query and chunk
• • Combine keyword filters with similarity order in one query
• • Delete or replace the chunks of one source document
• • Order matches from several collections into one context

Constraints

• • Store only text that the user has marked for retention.
• • Return at most ten matches per search.
• • Report the collection name with every result.
• • Do not store credentials or personal data in a collection.

MCP schema

{
  "name": "vector_db",
  "input": {
    "type": "object",
    "required": [
      "action",
      "collection",
      "query"
    ],
    "properties": {
      "query": {
        "type": "string"
      },
      "top_k": {
        "type": "integer"
      },
      "action": {
        "enum": [
          "store",
          "search",
          "delete",
          "list"
        ]
      },
      "filters": {
        "type": "object"
      },
      "collection": {
        "type": "string"
      },
      "text_chunks": {
        "type": "array",
        "items": {
          "type": "string"
        }
      }
    }
  },
  "output": {
    "type": "object",
    "properties": {
      "count": {
        "type": "integer"
      },
      "matches": {
        "type": "array",
        "items": {
          "type": "object"
        }
      }
    }
  },
  "description": "Stores text chunks and returns the most similar content for a query."
}