Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Easy-to-setup local RAG server with minimal configuration
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。
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。
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。
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
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。
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。
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。
BASE_DIR envBASE_DIRS envIGNORE_PATHS envDB_PATH envCACHE_DIR envHF_ENDPOINT envMODEL_NAME envMAX_FILE_SIZE envRAG_MAX_DISTANCE envRAG_GROUPING envRAG_MAX_FILES envCHUNK_MIN_LENGTH envSTORE_IMAGES envEMBED_TITLE_PREFIX envEMBED_HEADING_PREFIX envRAG_DEVICE envRAG_DTYPE envRAG_HYBRID_WEIGHT envRAG_RERANK_CMD envRAG_RERANK_TIMEOUT_MS env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。
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。
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
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
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
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
パソコンで動き、代わりにローカルの設定を使います
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
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。
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。
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。
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)。
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。
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。
同じカテゴリには:Context7, Codebase Memory, Exa
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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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。
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.
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?
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.
| Input | How to ingest |
|---|---|
| PDF, DOCX, TXT, Markdown | File ingestion or directory sync |
| HTML already fetched by the client | ingest_data |
| Plain text or Markdown held in memory | ingest_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.
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
| Tool | Purpose |
|---|---|
sync_start | Reconcile the index with all configured roots or one path |
sync_status | Poll a running sync job |
ingest_file | Ingest or replace one file |
ingest_data | Ingest text, Markdown, or HTML already held by the client |
query_documents | Search with semantic matching and keyword boost |
read_chunk_neighbors | Read surrounding chunks from a search result |
list_files | Show supported files and their ingestion state |
delete_file | Delete an indexed file or an ingest_data item |
status | Show index and search status |
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 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.
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 Variable | CLI Flag | Default | Description |
|---|---|---|---|
BASE_DIR | --base-dir | Current directory | One document root; the CLI flag is repeatable on ingest, list, and sync |
BASE_DIRS | N/A | (unset) | JSON array of document roots; takes precedence over BASE_DIR |
IGNORE_PATHS | N/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_ENDPOINT | N/A | https://huggingface.co | Hugging Face model download endpoint; use a mirror URL when direct downloads are blocked |
MAX_FILE_SIZE | --max-file-size | 104857600 (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 Variable | CLI Flag | Default | Description |
|---|---|---|---|
MODEL_NAME | --model-name | Xenova/all-MiniLM-L6-v2 | Hugging Face embedding model |
CHUNK_MIN_LENGTH | --chunk-min-length | 50 | Minimum 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_PREFIX | N/A | false | Add the document title to each chunk's embedding input |
EMBED_HEADING_PREFIX | N/A | false | Add the heading hierarchy to each chunk's embedding input when it fits |
RAG_DEVICE | N/A | cpu | ONNX Runtime execution device |
RAG_DTYPE | N/A | fp32 | Embedding 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.
###
冒頭だけを表示しています。続きはリポジトリで読めます