
OCDS German Procurement Search
io.github.qune-tech由 GitHub 帳號 qune-tech 發布v0.4.0更新於 Oct 24, 2026
Search and match German public procurement tenders (OCDS) from your local LLM.
概覽
讓助理透過在本機執行的伺服器與付費 API 金鑰,搜尋並媒合德國公共採購招標(OCDS)。
- 可以做什麼
- 提供十一個工具處理德國公共採購資料:對招標進行語意文字搜尋,依階段、CPV 前綴、國家、金額區間、截止日期、採購方與採購方式篩選瀏覽,取得某個 OCID 的原始 eForms XML,以及查看公告沿革(PIN 到 CN 到 CAN)。它也能管理本機公司設定檔,並依語意相似度將設定檔與招標媒合。查詢與設定檔文字在本機以多語言 ONNX 模型產生向量,只有向量、OCID、篩選值與 API 金鑰會傳送到 API。
- 適用情境
- 當助理需要查找、篩選或追蹤德國公共招標,或判斷哪些招標符合某家公司設定檔時使用。適合希望查詢與設定檔文字留在本機、同時仍使用廠商搜尋 API 的使用者。
- 執行需求
- 需要本機執行環境:可透過 npx 執行 npm 套件 @qune-tech/ocds-mcp、使用預先建置的執行檔,或從原始碼以 Rust 建置。需要廠商儀表板核發的 API 金鑰(OCDS_API_KEY,也接受 VERGABE_API_KEY),且需訂閱每月 99 歐元的 Pro 方案。需要能連線 API 的網路,以及約 120 MB 磁碟空間用於首次執行時下載的 ONNX 嵌入模型。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 OCDS German Procurement Search,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
vergabe-mcp
Local MCP server for German public procurement search. Connects your AI assistant (Claude, GPT, etc.) to the Vergabe Dashboard API for semantic search, tender matching, and company-profile management.
Your queries and company profiles never leave your machine. They are embedded locally with a multilingual ONNX model; only the resulting embedding vectors, OCIDs, and filter values are sent to the API. Data minimisation by design.
Hosted alternative: if you just want search from your AI assistant, there is a hosted MCP connector (OAuth sign-in, no installation) — see the setup guide. This local server is for running the client in your own environment: queries and profiles are embedded locally, only vectors and filter values leave the machine, and it unlocks API-key features.
New to German public procurement? The Vergabe Dashboard knowledge base explains eForms, EU thresholds, and the tender lifecycle, and KI für Vergabe covers the hosted AI side of this server.
Quick Start
1. Get an API key
Sign up at vergabe-dashboard.qune.de and create an API key. API keys require an active Pro plan (€99/month; the local server itself is free and open source — the API gate rides key issuance).
2. Install
Via npx (easiest — downloads the correct binary automatically):
Or download a pre-built binary from GitHub Releases:
Linux / macOS:
Windows: Extract the zip and move vergabe-mcp.exe somewhere on your PATH.
Or build from source:
3. Configure your AI client
Claude Desktop — edit claude_desktop_config.json:
Using npx:
Using the binary directly:
Claude Code — add .mcp.json to your project root:
Using npx:
Using the binary directly:
Cursor — Settings → MCP Servers → Add:
Using npx:
- Command:
npx - Args:
-y @qune-tech/vergabe-mcp --api-key sk_live_YOUR_KEY_HERE
Using the binary directly:
- Command:
vergabe-mcp - Args:
--api-key sk_live_YOUR_KEY_HERE
LM Studio — Settings → MCP → Add Server:
- Click + Add Server and choose STDIO
- Fill in:
Using npx:
- Name:
vergabe - Command:
npx - Arguments:
-y @qune-tech/vergabe-mcp --api-key sk_live_YOUR_KEY_HERE
Using the binary directly:
- Name:
vergabe - Command: full path to the binary, e.g.
/usr/local/bin/vergabe-mcp - Arguments:
--api-key sk_live_YOUR_KEY_HERE
- Click Save
- In the chat, select a model that supports tool use and enable the
vergabeserver
LM Studio requires models with tool-calling support (e.g. Qwen 2.5, Mistral, Llama 3.1+). Smaller models may not use all 11 tools reliably — 7B+ recommended.
Replace sk_live_YOUR_KEY_HERE with your actual API key. You can also pass the key via the VERGABE_API_KEY environment variable.
Available Tools (11)
CLI Options
How It Works
The MCP server runs locally on your machine:
- Company profiles (name, description, CPV interests, location) are stored in a local SQLite database — they never leave your network.
- Text embeddings are computed locally with a multilingual ONNX model (multilingual-e5-small, 384-dim). Search queries use the
query:prefix and go toPOST /api/v1/search/vector; profile descriptions use thepassage:prefix and go toPOST /api/v1/match/vector. - Only embedding vectors (384 floats), the OCIDs you fetch, filter values, and your API key are sent to the API. Your query and profile text stay local.
Privacy & data flow
What stays on your machine: profile text, search-query text, CPV interests, location.
What the API sees: embedding vectors, the OCIDs you read, the filter values you use, and your API key. These reveal commercial interest (which sectors, buyers, value bands, tenders you look at) but not the underlying text. Embedding vectors are a derived, pseudonymous representation — minimisation, not elimination.
First-run model download: on first use the server downloads the embedding model from huggingface.co:
- What:
model.onnx+tokenizer.json, ~118 MB total. - When: the first time the embedder runs; cached afterwards at
~/.cache/vergabe/models/multilingual-e5-small. - From:
huggingface.co(a US-operated third party). No user data is sent in this fetch — it is a plain model download.
For air-gapped / enterprise installs, place model.onnx and tokenizer.json in the cache directory above and the runtime download is skipped.
Requirements
- An API key from vergabe-dashboard.qune.de on the Pro plan (€99/month)
- ~120 MB disk space for the ONNX model (downloaded automatically on first run)
- Internet connection to reach the API
License
MIT — see LICENSE.
來源:README.md,提交 fded769
工具
0版本歷史
1- v0.4.0最新Sep 16, 2026
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