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RAG memory for LLM agents over Garnet Vector Sets: store text as embeddings, recall by meaning.
garnet-mcp 是 ChatHome Skills 收录的 MCP 条目，来源为 Official MCP Registry，质量分 73/100。 作为 Model Context Protocol 服务器，它向 Claude、Cursor、Copilot 等兼容 MCP 的 LLM 客户端暴露可直接调用的工具，让 agent 的能力超出模型内置范围。 该条目由 ChatHome Skills 的每日抓取从官方 registry 和 GitHub 发现查询中收录，每次运行都会重新核验仓库事实、新鲜度信号和安装元数据。 质量分综合了官方 registry 收录情况、可用安装命令、许可证完整性、仓库活跃度和 GitHub star 趋势，完整算法说明见来源页。安装命令已列于上方，可直接复制使用。 在将 garnet-mcp 加入生产 agent 工作流前，请核对上方的最近更新日期和仓库状态——下方事实快照是最近一次抓取的时点值。
dnx GarnetMcp.Server@0.1.2 --yes运行此命令前，请先核对上游源码。
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-08-10T19:37:51.200207Z",
"publishedAt": "2026-08-10T19:37:51.200207Z",
"statusChangedAt": "2026-08-10T19:37:51.200207Z"
}
},
"server": {
"name": "io.github.tarazou9/garnet-mcp",
"title": "garnet-mcp",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-10-17/server.schema.json",
"version": "0.1.2",
"packages": [
{
"version": "0.1.2",
"transport": {
"type": "stdio"
},
"identifier": "GarnetMcp.Server",
"registryType": "nuget",
"registryBaseUrl": "https://api.nuget.org/v3/index.json",
"environmentVariables": [
{
"name": "Garnet__Host",
"value": "{garnet_host}",
"variables": {
"garnet_host": {
"description": "Hostname of the Garnet (or any Redis-compatible) server. Defaults to 127.0.0.1 when left blank."
}
}
},
{
"name": "Garnet__Port",
"value": "{garnet_port}",
"variables": {
"garnet_port": {
"description": "Port of the Garnet server. Defaults to 6379 when left blank."
}
}
},
{
"name": "Embeddings__Provider",
"value": "{embeddings_provider}",
"variables": {
"embeddings_provider": {
"description": "Embedding provider: 'AzureOpenAI' for real embeddings, or 'Fake' for a deterministic offline provider (no external calls)."
}
}
},
{
"name": "Embeddings__Endpoint",
"value": "{embeddings_endpoint}",
"variables": {
"embeddings_endpoint": {
"description": "Azure OpenAI endpoint, e.g. https://YOUR-RESOURCE.openai.azure.com/ (required when Embeddings__Provider=AzureOpenAI). Authenticated keyless via DefaultAzureCredential."
}
}
},
{
"name": "Embeddings__DeploymentName",
"value": "{embeddings_deployment}",
"variables": {
"embeddings_deployment": {
"description": "Azure OpenAI embedding deployment name, e.g. text-embedding-3-small."
}
}
},
{
"name": "Embeddings__Dimensions",
"value": "{embeddings_dimensions}",
"variables": {
"embeddings_dimensions": {
"description": "Embedding vector dimensions, e.g. 1536 for text-embedding-3-small."
}
}
}
]
}
],
"repository": {
"url": "https://github.com/tarazou9/garnet-mcp",
"source": "github"
},
"description": "RAG memory for LLM agents over Garnet Vector Sets: store text as embeddings, recall by meaning."
}
}