
OCDS German Procurement Search
io.github.qune-tech由 GitHub 账号 qune-tech 发布v0.4.0更新于 Oct 24, 2026
Search and match German public procurement tenders (OCDS) from your local LLM.
概览
让助手通过本地运行的服务器和付费 API 密钥搜索并匹配德国公共采购招标(OCDS)。
- 可以做什么
- 提供十一个工具处理德国公共采购数据:对招标进行语义文本搜索,按阶段、CPV 前缀、国家、金额区间、截止日期、采购方和采购方式筛选浏览,获取某个 OCID 的原始 eForms XML,以及查看公告沿革(PIN 到 CN 到 CAN)。它还能管理本地公司档案,并按语义相似度将档案与招标匹配。查询和档案文本在本地用多语言 ONNX 模型生成向量,只有向量、OCID、筛选值和 API 密钥会发送到 API。
- 适用场景
- 当助手需要查找、筛选或跟踪德国公共招标,或判断哪些招标符合某家公司档案时使用。适合希望查询和档案文本留在本机、同时仍使用厂商搜索 API 的用户。
- 运行要求
- 需要本地运行环境:可通过 npx 运行 npm 包 @qune-tech/ocds-mcp、使用预编译二进制文件,或从源码用 Rust 构建。需要厂商仪表板签发的 API 密钥(OCDS_API_KEY,也接受 VERGABE_API_KEY),且需订阅每月 99 欧元的 Pro 方案。需要能访问 API 的网络,以及约 120 MB 磁盘空间用于首次运行时下载的 ONNX 嵌入模型。
安装
在 SourceWeft 中
- 打开 控制台中的 OCDS German Procurement Search,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
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.
来源:README.md,提交 fded769
工具
0版本历史
1- v0.4.0最新Sep 16, 2026
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