ai.sovantica/engrava

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-10-02T21:18:52.171886Z",
      "publishedAt": "2026-10-02T21:18:52.171886Z",
      "statusChangedAt": "2026-10-02T21:18:52.171886Z"
    }
  },
  "server": {
    "name": "ai.sovantica/engrava",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "version": "0.7.0",
    "packages": [
      {
        "version": "0.7.0",
        "transport": {
          "type": "stdio"
        },
        "identifier": "engrava-mcp",
        "registryType": "pypi",
        "environmentVariables": [
          {
            "name": "ENGRAVA_DB_PATH",
            "format": "filepath",
            "isRequired": true,
            "description": "Absolute path to the SQLite database file. Created if missing; its directory must exist. Enough on its own: full-text search, the graph and MindQL work, but vector search needs ENGRAVA_MCP_CONFIG."
          },
          {
            "name": "ENGRAVA_MCP_CONFIG",
            "format": "filepath",
            "description": "Absolute path to an engrava.yaml, for an embedding provider (vector search) and the thought/edge journal. When set, it takes precedence and ENGRAVA_DB_PATH is not read."
          },
          {
            "name": "ENGRAVA_MCP_READ_ONLY",
            "format": "boolean",
            "description": "Set to true to register no write tools."
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/sovantica/engrava-mcp",
      "source": "github"
    },
    "description": "MindQL queries, hybrid search, and a thought graph - read/write memory for AI agents."
  }
}