Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
DigiKey and Mouser MCP server for component research, BOM analysis, and sourcing.
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。
DIGIKEY_ACCESS_TOKEN, DIGIKEY_CLIENT_ID, DIGIKEY_CLIENT_SECRET, MOUSER_SEARCH_API_KEY, MOUSER_ACCOUNT_API_KEY「秘密」と表示された値は、あなたのアカウントの鍵です。絶対に人に教えたり、チャットに貼り付けたりしないでください。
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": {
"partuno": {
"command": "uvx",
"args": [
"partuno",
"partuno-mcp"
],
"env": {
"DIGIKEY_ACCESS_TOKEN": "your-value",
"DIGIKEY_CLIENT_ID": "your-value",
"DIGIKEY_CLIENT_SECRET": "your-value",
"MOUSER_SEARCH_API_KEY": "your-value",
"MOUSER_ACCOUNT_API_KEY": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add partuno --env DIGIKEY_ACCESS_TOKEN=your-value --env DIGIKEY_CLIENT_ID=your-value --env DIGIKEY_CLIENT_SECRET=your-value --env MOUSER_SEARCH_API_KEY=your-value --env MOUSER_ACCOUNT_API_KEY=your-value -- uvx partuno partuno-mcp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": {
"partuno": {
"command": "uvx",
"args": [
"partuno",
"partuno-mcp"
],
"env": {
"DIGIKEY_ACCESS_TOKEN": "your-value",
"DIGIKEY_CLIENT_ID": "your-value",
"DIGIKEY_CLIENT_SECRET": "your-value",
"MOUSER_SEARCH_API_KEY": "your-value",
"MOUSER_ACCOUNT_API_KEY": "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": {
"partuno": {
"type": "stdio",
"command": "uvx",
"args": [
"partuno",
"partuno-mcp"
],
"env": {
"DIGIKEY_ACCESS_TOKEN": "your-value",
"DIGIKEY_CLIENT_ID": "your-value",
"DIGIKEY_CLIENT_SECRET": "your-value",
"MOUSER_SEARCH_API_KEY": "your-value",
"MOUSER_ACCOUNT_API_KEY": "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。
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 JPMarhefka 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。
入力が必要なもの:DIGIKEY_ACCESS_TOKEN, DIGIKEY_CLIENT_ID, DIGIKEY_CLIENT_SECRET, MOUSER_SEARCH_API_KEY, MOUSER_ACCOUNT_API_KEY
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(4.0.1)がレジストリに届いたのは3 か月前です。
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Python (uv)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。
Partuno is an open-source, provider-neutral, local-first Model Context Protocol (MCP) server for electronic component research, BOM analysis, sourcing comparison, and safe distributor workflows. Connect an AI assistant to your own DigiKey and Mouser credentials without shared credentials or autonomous ordering, while keeping evidence, normalized attributes, and explicit uncertainty visible.
Partuno was initially developed as a native MCP server. Native MCP is the recommended connection because it works as an app in ordinary ChatGPT conversations and exposes richer tool annotations. Existing Custom GPT Action endpoints remain available for backward compatibility, including /action-openapi.json.
Partuno does not provide a shared public server or shared distributor
credentials. The Render configuration is an optional reference deployment
for an operator's own single-user instance.
Partuno is published on PyPI and listed in the official MCP Registry. The recommended local MCP launcher is:
uvx --from partuno partuno-mcp
This downloads the current published release and runs the default local stdio transport. To pin the current release exactly, use:
uvx --from 'partuno==4.0.1' partuno-mcp
MCP clients that accept a local command can use uvx with these arguments:
{
"command": "uvx", "args": ["--from", "partuno", "partuno-mcp"] }
Keep your operator-owned DigiKey and Mouser credentials in the MCP client's environment. See the local deployment guide for the supported variables and the native MCP setup guide for operator-owned remote deployments. For a hosted directory listing, see Partuno on Glama.
If uvx is not available, install the published package into an existing Python environment:
python3 -m pip install partuno
partuno-mcp
Develop from source
Use a checkout only when contributing, running unreleased changes, or needing the repository's .env.example and test suite:
git clone https://github.com/JPMarhefka/partuno.git
cd partuno python3 -m venv .venv source .venv/bin/activate python -m pip install -r requirements.txt cp .env.example .env set -a source .env set +a python -m partuno
See the local deployment guide for loopback HTTP and source-checkout details.
If your client requires a reachable HTTPS endpoint, deploy your own instance with your own provider accounts, OAuth application, signing key, rate limits, and hosting budget. Start with the Render reference deployment, or use the Docker and Azure guides. There is no shared Partuno endpoint intended for public use.
Custom GPT Actions are retained for existing integrations. The Action/OpenAPI surface is not the primary architecture; use native MCP for new connections. The Action setup details are documented in MCP setup.
meets, does_not_meet, and unknown evidence states.See the capability reference for the current scope and the safety boundary for each workflow.
Partuno can run as a local-first DigiKey MCP server for component search, product research, current pricing and availability, lifecycle checks, MyLists, quotes, receiving reconciliation, and other bounded account workflows. The DigiKey MCP server guide covers setup, supported workflows, and the provider-specific safety boundaries.
Partuno also provides a Mouser MCP server surface for catalog search, exact offer comparison, read-only order history, and preview-confirmed Cart workflows. The Mouser MCP server guide explains the separate operator-owned Mouser credentials and what the server does and does not execute.
Use Partuno as an electronics BOM MCP server to consolidate duplicate rows, resolve exact products, estimate current merchandise cost, check stock and lifecycle risk, inspect compliance and lead-time signals, and find substitutes or alternate packaging. See the electronics BOM MCP server guide for a complete read-only example and known limitations.
Partuno turns a natural-language engineering request into a traceable sourcing workflow. Recommendations preserve meets, does_not_meet, and unknown evidence states, so missing or ambiguous attributes are not silently treated as qualified requirements. The natural-language examples show the intended interaction style.
For an exact comparison, Partuno matches manufacturer and MPN identity first, then compares requested-quantity pricing, minimum order quantity, order multiples, purchasable quantity, availability, and manufacturer lead time. If a provider fails or returns incomplete evidence, the result remains partial instead of inventing a winner. See the DigiKey vs Mouser comparison guide.
Once Partuno is connected, a user can simply ask:
I’m choosing a dual op amp for a 3.3 V circuit. Find a few LM358-family
options that can operate at 3.3 V, are RoHS compliant, normally stocked, and available in a quantity of 10. Show the evidence behind each recommendation and keep uncertain data clearly marked.
Partuno turns that plain-language request into a provider-aware result with qualified, unverified, and rejected candidates; normalized voltage and compliance evidence; cross-distributor availability; quantity pricing; and a Pareto shortlist. It does not silently treat missing specifications as a pass.
This is a roughly 10-second prompt-to-result capture that shows the request and a smooth response scroll. Prices, stock, and lead times are live provider data and can change.
See more natural-language examples for comparison, product research, BOM review, and safe account-preview workflows.
| Topic | Guide |
|---|---|
| Capabilities and safety boundaries | Capability reference |
| Native MCP connection | MCP setup |
| Local stdio or loopback HTTP | Local deployment |
| Docker deployment | Docker deployment |
| Operator-owned Render deployment | Render reference |
| Azure deployment | Azure deployment |
| Security and credential handling | Security guide |
| Offline and live validation | Testing guide and validation notes |
| Natural-language usage examples | Examples |
| DigiKey, Mouser, and BOM entry points | Search-focused guides |
| Contributing | CONTRIBUTING.md |
| Security reports | SECURITY.md |
| Release history | CHANGELOG.md |
The full-resolution Partuno logo is available for project pages, client listings, and integration documentation. A compact plugin icon is also included for client or plugin upload fields with strict PNG size limits.
The compact icon is intentionally optimized for small upload limits. If a host accepts larger assets, use the full-resolution logo instead. The compact icon can also be downloaded directly from the repository's raw asset URL.
When using the logo in a ChatGPT app, MCP client, or other plugin integration, identify the integration as Partuno and do not imply endorsement by DigiKey, Mouser, or another provider. Check the host platform's current dimensions, format, and file-size requirements before uploading.
| Mode | Best for | Credential ownership |
|---|---|---|
| Local stdio | Desktop MCP clients and personal development | Operator's local environment |
| Loopback HTTP | Clients that cannot launch stdio | Operator's local environment |
| Remote HTTPS | A self-hosted ChatGPT/Codex connection | Operator's own deployment and accounts |
Remote mode adds OAuth and HTTPS because a hosted MCP endpoint must authenticate the client and protect upstream access. It is optional; local mode remains the default and does not require Partuno OAuth.
Partuno 4.0.1 is an active open-source release. The test suite covers offline provider contracts, MCP tool behavior, REST compatibility, credential safety, normalization, and the multi-distributor workflows. Live provider calls are opt-in because they require operator credentials and are subject to provider quotas.
See testing before running live smoke tests.
Partuno is released under the Apache License 2.0. See NOTICE for project attribution and independence statements. Partuno is not affiliated with, endorsed by, or sponsored by DigiKey, Mouser, or any manufacturer or distributor named in the project. Their names and marks identify integrations only.