プロトタイプ · プレビュー版:公式レジストリのすべてのサーバーを掲載し、30分ごとに同期しています。レビューとアカウント機能は今後のステップで始まります。
PopNx MCPAIを使う人のためのMCPサーバー · やさしい言葉で解説

MCP Local Rag

作者:shinprバージョン 0.23.2i★ –iパソコンで動く
レジストリ名io.github.shinpr/mcp-local-rag

インストール

概要

作者による説明(英語の原文)

Easy-to-setup local RAG server with minimal configuration

PopNx MCPの3つの柱

この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。

できること

このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。

  • 接続方法: パソコンで動く
  • トランスポート: stdio
  • カテゴリ: 検索・AIの記憶

インストール

使っているAIアプリを選んで、設定をコピーしてください。

インストールの前に

  • 基本チェック:1項目中1項目に合格
  • あなたのパソコンで、あなたのユーザーと同じ権限(ファイル、ネットワーク、アプリ)で動きます。
  • 自分で入力するもの: 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

必要なもの

  • npmNode.jsのインストール(npxコマンドに必要)

どのアプリを使っていますか?

上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。

Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます

{
  "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"
      }
    }
  }
}

ターミナルでこれを実行します

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

動かす前に、空欄の値を入力してください。

Cursorで開く

または、設定を自分でコピーする

プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます

{
  "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"
      }
    }
  }
}

ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。

VS Codeで開く

または、設定を自分でコピーする

プロジェクトの .vscode/mcp.json に貼り付けます

{
  "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"
      }
    }
  }
}

ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。

これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。

選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。

自分で入力する値

  • 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.

この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。

できること

パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。

基本チェックi

誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック

  • ✓ 合格

    作者とリポジトリが一致

    レジストリ名は、リポジトリを所有するGitHubアカウント shinpr で確認済みです

  • … 未チェック

    今もメンテナンスされている

    このリポジトリはまだGitHubから読み取っていません

  • … 未チェック

    ライセンスがある

    このリポジトリはまだGitHubから読み取っていません

  • – 対象外

    エンドポイントが応答する

    これはパソコンで動くため、インターネット上のエンドポイントはありません

  • – 対象外

    OAuthでサインイン

    パソコンで動き、代わりにローカルの設定を使います

  • … 未チェック

    ツールの説明に隠れた指示がない

    パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです

検索・AIの記憶のほかのサーバーとの比較i

  • このカテゴリのほかの5654件のうち0%よりスターが多い
  • このカテゴリのほかの5654件のうち94%より最近レジストリに公開
  • 基本チェックの結果が、このカテゴリのほかの5654件のうち100%と同じかそれ以上

レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。

よくある質問

MCP Local Ragは安全ですか?

当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。

どんなキーやコードが必要ですか?

レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。

パソコンで動きますか、それともクラウドですか?

あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。

今もメンテナンスされていますか?

GitHubリポジトリの最終更新は–、最新バージョン(0.23.2)がレジストリに届いたのは3 日前です。

どのアプリで使えますか?

Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。

代わりに使えるものは?

同じカテゴリには:Context7, Codebase Memory, Exa

代わりの候補

同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。

カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i

Context7

作者:upstash

Up-to-date docs for AI agents

パソコンでもクラウドでも★ 64,224MIT昨日更新
インストール

Codebase Memory

作者:DeusData

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

パソコンで動く★ 48,148MIT21 日前更新
インストール

Exa

作者:exa-labs

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

クラウドで動く★ 5,213MIT2 か月前更新
インストール

作者のREADME

READMEを開く

作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。

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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