CPersona

{
  "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
  "name": "io.github.Cloto-dev/cpersona",
  "description": "Persistent AI memory in one SQLite file: 3-layer hybrid search, confidence scoring, 29 tools.",
  "title": "CPersona",
  "repository": {
    "url": "https://github.com/Cloto-dev/cpersona",
    "source": "github"
  },
  "version": "2.5.3",
  "websiteUrl": "https://github.com/Cloto-dev/cpersona#readme",
  "packages": [
    {
      "registryType": "pypi",
      "registryBaseUrl": "https://pypi.org",
      "identifier": "cpersona",
      "version": "2.5.3",
      "runtimeHint": "uvx",
      "transport": {
        "type": "stdio"
      },
      "environmentVariables": [
        {
          "description": "SQLite database path. Defaults to data/cpersona.db, which follows the client's working directory — set it to an absolute path to keep one memory across sessions.",
          "default": "data/cpersona.db",
          "name": "CPERSONA_DB_PATH"
        },
        {
          "description": "Embedding mode: 'http' to use an embedding server for vector search, 'none' for lexical layers only.",
          "default": "none",
          "name": "CPERSONA_EMBEDDING_MODE"
        },
        {
          "description": "Embedding server endpoint, e.g. http://127.0.0.1:8401/embed. Required when CPERSONA_EMBEDDING_MODE=http.",
          "name": "CPERSONA_EMBEDDING_URL"
        },
        {
          "description": "Recall fusion strategy: 'rrf' (default), 'rsf', or 'cascade'.",
          "default": "rrf",
          "name": "CPERSONA_RECALL_MODE"
        }
      ]
    }
  ]
}