原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

MCP Local Rag

作者:shinpr版本 0.23.2i★ –i在你的电脑上运行
注册名称io.github.shinpr/mcp-local-rag

安装

概览

作者提供的说明(英文原文)

Easy-to-setup local RAG server with minimal configuration

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 需要你自己填写: BASE_DIR, BASE_DIRS, IGNORE_PATHS, DB_PATH, CACHE_DIR, HF_ENDPOINT, MODEL_NAME, MAX_FILE_SIZE, RAG_MAX_DISTANCE, RAG_GROUPING, RAG_MAX_FILES, CHUNK_MIN_LENGTH, STORE_IMAGES, EMBED_TITLE_PREFIX, EMBED_HEADING_PREFIX, RAG_DEVICE, RAG_DTYPE, RAG_HYBRID_WEIGHT, RAG_RERANK_CMD, RAG_RERANK_TIMEOUT_MS

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "local-rag": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-local-rag"
      ],
      "env": {
        "BASE_DIR": "your-value",
        "BASE_DIRS": "your-value",
        "IGNORE_PATHS": "your-value",
        "DB_PATH": "your-value",
        "CACHE_DIR": "your-value",
        "HF_ENDPOINT": "your-value",
        "MODEL_NAME": "your-value",
        "MAX_FILE_SIZE": "your-value",
        "RAG_MAX_DISTANCE": "your-value",
        "RAG_GROUPING": "your-value",
        "RAG_MAX_FILES": "your-value",
        "CHUNK_MIN_LENGTH": "your-value",
        "STORE_IMAGES": "your-value",
        "EMBED_TITLE_PREFIX": "your-value",
        "EMBED_HEADING_PREFIX": "your-value",
        "RAG_DEVICE": "your-value",
        "RAG_DTYPE": "your-value",
        "RAG_HYBRID_WEIGHT": "your-value",
        "RAG_RERANK_CMD": "your-value",
        "RAG_RERANK_TIMEOUT_MS": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add local-rag --env BASE_DIR=your-value --env BASE_DIRS=your-value --env IGNORE_PATHS=your-value --env DB_PATH=your-value --env CACHE_DIR=your-value --env HF_ENDPOINT=your-value --env MODEL_NAME=your-value --env MAX_FILE_SIZE=your-value --env RAG_MAX_DISTANCE=your-value --env RAG_GROUPING=your-value --env RAG_MAX_FILES=your-value --env CHUNK_MIN_LENGTH=your-value --env STORE_IMAGES=your-value --env EMBED_TITLE_PREFIX=your-value --env EMBED_HEADING_PREFIX=your-value --env RAG_DEVICE=your-value --env RAG_DTYPE=your-value --env RAG_HYBRID_WEIGHT=your-value --env RAG_RERANK_CMD=your-value --env RAG_RERANK_TIMEOUT_MS=your-value -- npx -y mcp-local-rag

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "local-rag": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-local-rag"
      ],
      "env": {
        "BASE_DIR": "your-value",
        "BASE_DIRS": "your-value",
        "IGNORE_PATHS": "your-value",
        "DB_PATH": "your-value",
        "CACHE_DIR": "your-value",
        "HF_ENDPOINT": "your-value",
        "MODEL_NAME": "your-value",
        "MAX_FILE_SIZE": "your-value",
        "RAG_MAX_DISTANCE": "your-value",
        "RAG_GROUPING": "your-value",
        "RAG_MAX_FILES": "your-value",
        "CHUNK_MIN_LENGTH": "your-value",
        "STORE_IMAGES": "your-value",
        "EMBED_TITLE_PREFIX": "your-value",
        "EMBED_HEADING_PREFIX": "your-value",
        "RAG_DEVICE": "your-value",
        "RAG_DTYPE": "your-value",
        "RAG_HYBRID_WEIGHT": "your-value",
        "RAG_RERANK_CMD": "your-value",
        "RAG_RERANK_TIMEOUT_MS": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "local-rag": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "mcp-local-rag"
      ],
      "env": {
        "BASE_DIR": "your-value",
        "BASE_DIRS": "your-value",
        "IGNORE_PATHS": "your-value",
        "DB_PATH": "your-value",
        "CACHE_DIR": "your-value",
        "HF_ENDPOINT": "your-value",
        "MODEL_NAME": "your-value",
        "MAX_FILE_SIZE": "your-value",
        "RAG_MAX_DISTANCE": "your-value",
        "RAG_GROUPING": "your-value",
        "RAG_MAX_FILES": "your-value",
        "CHUNK_MIN_LENGTH": "your-value",
        "STORE_IMAGES": "your-value",
        "EMBED_TITLE_PREFIX": "your-value",
        "EMBED_HEADING_PREFIX": "your-value",
        "RAG_DEVICE": "your-value",
        "RAG_DTYPE": "your-value",
        "RAG_HYBRID_WEIGHT": "your-value",
        "RAG_RERANK_CMD": "your-value",
        "RAG_RERANK_TIMEOUT_MS": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • BASE_DIR env
    Base directory for document storage (defaults to current working directory). Ignored when BASE_DIRS is set.
  • BASE_DIRS env
    JSON array of base directories (e.g. '["/a","/b"]'). Takes precedence over BASE_DIR.
  • IGNORE_PATHS env
    JSON array of glob patterns for files to keep out of the index, each matched against the file's path relative to its root (e.g. '["plugs/**","**/*.draft.md"]').
  • DB_PATH env
    Path to LanceDB database directory (defaults to ./lancedb/)
  • CACHE_DIR env
    Directory where Transformers.js models are cached (defaults to ./models/)
  • HF_ENDPOINT env
    Hugging Face model download endpoint. Set this to a mirror URL when direct downloads are blocked (defaults to https://huggingface.co).
  • MODEL_NAME env
    Embedding model name (defaults to Xenova/all-MiniLM-L6-v2)
  • MAX_FILE_SIZE env
    Maximum file size in bytes (defaults to 104857600 / 100MB)
  • RAG_MAX_DISTANCE env
    Maximum distance threshold for filtering search results. Results with distance greater than this value will be excluded. Lower values mean stricter filtering (e.g., 0.5 for high relevance only)
  • RAG_GROUPING env
    Grouping mode for quality filtering. 'similar' returns only the most similar group (stops at first distance jump). 'related' includes related groups (stops at second distance jump). Unset means no gr…
  • RAG_MAX_FILES env
    Maximum number of files to keep in search results. Results are filtered to include only chunks from the top N best-scoring files. For example, 1 returns only the single best-matching file's chunks. U…
  • CHUNK_MIN_LENGTH env
    Minimum chunk length in characters (1-10000, defaults to 50). Chunks shorter than this threshold are filtered out during ingestion.
  • STORE_IMAGES env
    Store supported PDF and DOCX images during ingestion and return them with matched chunks (defaults to false).
  • EMBED_TITLE_PREFIX env
    Embed each chunk together with its document title, which can help when passages don't restate the topic the title names (defaults to false). After changing it, use a new DB_PATH or delete the index a…
  • EMBED_HEADING_PREFIX env
    Add section headings to chunk embeddings when they fit (defaults to false). Independent of EMBED_TITLE_PREFIX. Re-ingest documents after changing it.
  • RAG_DEVICE env
    Execution device for the embedder (defaults to cpu). Passed straight to ONNX Runtime; see the Transformers.js device source for the supported backend names. If the requested device fails to initializ…
  • RAG_DTYPE env
    Embedding quantization dtype for the embedder (defaults to fp32). Opt-in and pass-through; accepts any dtype the chosen model provides (fp32, fp16, q8, int8, ...). If the model has no variant for the…
  • RAG_HYBRID_WEIGHT env
    Keyword boost factor for hybrid search (0.0-1.0, defaults to 0.6). 0 means semantic similarity only; higher values increase the keyword-match contribution to the final score.
  • RAG_RERANK_CMD env
    External reranker command template. Use {query} and {top} for query text and result count; unset disables reranking.
  • RAG_RERANK_TIMEOUT_MS env
    Time budget per rerank call in milliseconds (100-600000, defaults to 10000). On timeout the spawned command is killed and the pre-rerank ordering is returned.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 shinpr 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 5684 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 5684 个服务器的 92% 更新
  • 基础检查结果等于或优于此分类中其他 5684 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

MCP Local Rag 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.23.2)于3天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

Up-to-date docs for AI agents

作者:upstash广泛使用↓ 97.3万/周★ 6.4万前天更新

电脑或云端

Codebase Memory

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

作者:DeusData广泛使用↓ 1.8万/周★ 4.8万22天前更新

在你的电脑上运行

Exa

Connect AI agents to Exa for web search, content fetching, and multi-step research.

作者:exa-labs正在增长★ 52132个月前更新

在云端运行✓ exa.ai

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

MCP Local RAG

English | 简体中文 | Deutsch | Español | Português (Brasil) | Français

Search private documents from an MCP client or the terminal without sending them to an embedding API.

mcp-local-rag indexes PDF, DOCX, Markdown, and text files on your machine. Search combines semantic similarity with keyword matching, so queries can match both intent and exact technical terms such as API names, class names, and error codes. Results include source passages and, where available, headings, line numbers, or page numbers so you can check and cite the original document.

No API key, Docker, Python, or external database is required. After the initial model download, text ingestion and search work offline.

Quick Start

Requirements
  • Node.js 22.20 or later
  • Internet access on first use to download the npm package and embedding model
  • A directory containing the documents you want to search

Set BASE_DIR to that directory. It is also the security boundary for file operations. Replace /absolute/path/to/your/documents below with the directory's absolute path.

Use one of the examples below, or register npx -y mcp-local-rag and set BASE_DIR using your client's MCP configuration format.

Set DB_PATH and CACHE_DIR to absolute paths as well. Relative paths resolve from the server's working directory, so starting the server from different projects creates a separate index and model cache in each.

Claude Code

Run this command:

claude mcp add local-rag --scope user --env BASE_DIR=/absolute/path/to/your/documents -- npx -y mcp-local-rag

Codex

Add to ~/.codex/config.toml:

[mcp_servers.local-rag]

command = "npx" args = ["-y", "mcp-local-rag"]

[mcp_servers.local-rag.env] BASE_DIR = "/absolute/path/to/your/documents"

OpenCode

Add to ~/.config/opencode/opencode.json (or opencode.jsonc):

{

"$schema": "https://opencode.ai/config.json", "mcp": { "local-rag": { "type": "local", "command": ["npx", "-y", "mcp-local-rag"], "environment": { "BASE_DIR": "/absolute/path/to/your/documents" } } } }

Cursor

Add to ~/.cursor/mcp.json:

{

"mcpServers": { "local-rag": { "command": "npx", "args": ["-y", "mcp-local-rag"], "env": { "BASE_DIR": "/absolute/path/to/your/documents" } } } }

Restart the client, then ask it to build the index:

Sync all documents in the configured root and wait until it finishes.

The first sync downloads the default embedding model (about 90 MB) and may take 1–2 minutes before ingestion starts. Later runs use the local cache.

Once the sync completes:

What does the API documentation say about authentication?
CLI Quick Start

To use the CLI without an MCP client:

npx mcp-local-rag ingest ./docs/

npx mcp-local-rag query "authentication API"

The CLI uses the current directory as its document root by default. Run both commands from the same directory so they use the same default index, or set BASE_DIR and DB_PATH explicitly.

Supported Content

InputHow to ingest
PDF, DOCX, TXT, MarkdownFile ingestion or directory sync
HTML already fetched by the clientingest_data
Plain text or Markdown held in memoryingest_data with a stable source identifier

HTML fetching is not built into the server. An MCP client can fetch a page and pass its HTML to ingest_data.

Excel, PowerPoint, standalone images, and source-code file extensions are not supported by file ingestion. PDFs can optionally use a local vision model to describe figures, but this is not OCR or image search.

Using the Index

Sync after adding, editing, or removing documents. For searches and follow-up reading, ask your MCP client:

Find the documented behavior of ERR_CONNECTION_REFUSED.

Read the surrounding chunks for that result.

You can also ingest a single file or HTML already fetched by the client. To refresh an existing entry, ingest the document again using the same path or source identifier. sync skips unchanged files. MCP file paths must be absolute and inside a configured document root.

PDF section headings may be inaccurate. Check the original page when you need the exact heading.

MCP Tools

ToolPurpose
sync_startReconcile the index with all configured roots or one path
sync_statusPoll a running sync job
ingest_fileIngest or replace one file
ingest_dataIngest text, Markdown, or HTML already held by the client
query_documentsSearch with semantic matching and keyword boost
read_chunk_neighborsRead surrounding chunks from a search result
list_filesShow supported files and their ingestion state
delete_fileDelete an indexed file or an ingest_data item
statusShow index and search status

CLI

Use the CLI to update the index, narrow searches, or remove indexed content:

npx mcp-local-rag sync ./docs/

npx mcp-local-rag query "auth" --scope /docs/api --scope /docs/guide npx mcp-local-rag read-neighbors --file-path /abs/path.md --chunk-index 5 npx mcp-local-rag list npx mcp-local-rag status npx mcp-local-rag delete ./docs/old.pdf npx mcp-local-rag delete --source "https://example.com/docs"

ingest imports the selected files; sync also removes entries for deleted files and skips unchanged files. Use --scope to restrict search results to a path prefix, repeating it to include multiple prefixes.

Global options such as --db-path, --cache-dir, and --model-name go before the subcommand. Subcommand options go after it:

npx mcp-local-rag --db-path ./my-db query "authentication"

Run npx mcp-local-rag --help for the complete command reference.

query writes its results to stdout as JSON, best match first, so it can be piped into another tool. The field-by-field contract is in docs/schema/query-output.schema.json.

If you move a project and want to keep its index, stop the MCP server and other database writers, move the files and database together without changing the directory layout, then run:

npx mcp-local-rag --db-path /new/project/lancedb relocate --from /old/project --to /new/project

Update BASE_DIR/BASE_DIRS, DB_PATH, and any other moved paths in your MCP configuration, then restart the client.

Agent Skills

Agent Skills provide query and ingestion guidance for AI assistants:

npx mcp-local-rag skills install --claude-code

npx mcp-local-rag skills install --claude-code --global npx mcp-local-rag skills install --codex

Installed skills cover query formulation, result refinement, and HTML ingestion. Ask the assistant to use the mcp-local-rag skill explicitly if it does not activate automatically.

Advanced Options

Start with the defaults. Open the sections below when you need different document roots, better results for your corpus, or searchable PDF figures.

Storage and Document Roots

The MCP server reads environment variables. The CLI accepts the listed variables and flags. Keep the same DB_PATH when commands should use the same index.

Environment VariableCLI FlagDefaultDescription
BASE_DIR--base-dirCurrent directoryOne document root; the CLI flag is repeatable on ingest, list, and sync
BASE_DIRSN/A(unset)JSON array of document roots; takes precedence over BASE_DIR
IGNORE_PATHSN/A(unset)JSON array of glob patterns for files to keep out of the index
DB_PATH--db-path./lancedb/Vector database location
CACHE_DIR--cache-dir./models/Model cache directory
HF_ENDPOINTN/Ahttps://huggingface.coHugging Face model download endpoint; use a mirror URL when direct downloads are blocked
MAX_FILE_SIZE--max-file-size104857600 (100MB)Maximum file size in bytes

File operations stay within configured roots. For multiple directories, set BASE_DIRS='["/absolute/docs","/absolute/specs"]' or repeat CLI --base-dir. Precedence: CLI roots, BASE_DIRS, BASE_DIR, then the current directory. Only the highest-priority source is used; roots from different sources are not merged. Invalid BASE_DIRS is an error. Relative DB_PATH and CACHE_DIR are resolved from the working directory.

To keep files out of the index, set IGNORE_PATHS to a JSON array of glob patterns, for example IGNORE_PATHS='["plugs/**","**/*.draft.md"]'. Each pattern is matched against the file's path relative to the root that contains it, written with / on every OS. * and ** do not match names that start with ., so write such directories out, as in ".obsidian/**". ingest, list, and sync skip matching files when they scan a directory, and sync removes any that are already indexed. Invalid IGNORE_PATHS is an error.

Models and Search Tuning

Choose an embedding model for your documents’ language and subject. Compare settings using questions you actually ask and check which source passages are returned. The model must support mean pooling and L2 normalization, which this tool uses to produce embeddings.

Environment VariableCLI FlagDefaultDescription
MODEL_NAME--model-nameXenova/all-MiniLM-L6-v2Hugging Face embedding model
CHUNK_MIN_LENGTH--chunk-min-length50Minimum length in characters (1–10000) for ordinary chunks; a fragment of content split to fit the model's token limit can be shorter
EMBED_TITLE_PREFIXN/AfalseAdd the document title to each chunk's embedding input
EMBED_HEADING_PREFIXN/AfalseAdd the heading hierarchy to each chunk's embedding input when it fits
RAG_DEVICEN/AcpuONNX Runtime execution device
RAG_DTYPEN/Afp32Embedding dtype passed to the selected model

Both prefix options default to false and work independently. Try EMBED_TITLE_PREFIX when a passage needs the document’s overall topic, or EMBED_HEADING_PREFIX when it needs its section’s topic. Enabling both is not always better. They affect embeddings, not the returned text or keyword index; heading context is omitted when it would exceed the input budget.

When changing embedding models, build a fresh index at a new DB_PATH. Vectors from different models are not comparable, even when their dimensions match. After changing RAG_DTYPE or either prefix option, re-ingest all indexed documents before searching. sync skips unchanged files.

The CLI does not read MCP client configuration. When sharing an index, use the same model, RAG_DTYPE, and prefix settings for ingestion and search. A change to RAG_DEVICE alone does not require a new index.

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只显示了开头部分;其余内容请在仓库中阅读

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