Qdrant Memory

Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools.

What does this entry provide?

Qdrant Memory is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Qdrant Memory to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Official MCP Registry
Quality: 78/100
Install method: pypi
Repo: cnkang/qdrant-memory-mcp
Repo updated: Oct 24, 2026

Install command

uvx qdrant-memory-mcp==0.2.5

Install method: pypi

Review the upstream source before running this command.

GitHub project

Quality

Repo stars

Repo updated

Oct 24, 2026

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.

MCP manifest

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-10-24T09:44:51.122562Z",
      "publishedAt": "2026-10-24T09:44:51.122562Z",
      "statusChangedAt": "2026-10-24T09:44:51.122562Z"
    }
  },
  "server": {
    "name": "io.github.cnkang/qdrant-memory",
    "title": "Qdrant Memory",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "version": "0.2.5",
    "packages": [
      {
        "version": "0.2.5",
        "transport": {
          "type": "stdio"
        },
        "identifier": "qdrant-memory-mcp",
        "runtimeHint": "uvx",
        "registryType": "pypi",
        "registryBaseUrl": "https://pypi.org",
        "packageArguments": [
          {
            "name": "--home",
            "type": "named",
            "valueHint": "profile_path",
            "description": "Optional absolute path to an operator-configured, initialized profile; omission uses ~/.qdrant-memory."
          },
          {
            "type": "positional",
            "value": "mcp"
          }
        ],
        "environmentVariables": [
          {
            "name": "QDRANT_URL",
            "description": "Optional remote Qdrant endpoint when configured for server/cloud mode."
          },
          {
            "name": "QDRANT_API_KEY",
            "isSecret": true,
            "description": "Optional Qdrant credential for configured remote storage."
          },
          {
            "name": "EMBEDDING_API_KEY",
            "isSecret": true,
            "description": "Optional credential for the configured embedding provider."
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/cnkang/qdrant-memory-mcp",
      "source": "github"
    },
    "description": "Persistent scoped Qdrant memory for agents, through four explicit stdio MCP tools."
  }
}

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