systemlink-mcp

systemlink-mcp

Exposes NI SystemLink test-and-measurement data to AI clients via MCP, providing test-domain tools for yield by product revision, failing DUT steps versus spec limits, measurement-trace summaries, and calibration-due assets.

Category
访问服务器

README

systemlink-mcp

An MCP server that exposes NI SystemLink to AI clients with test-and-measurement-native tools: yield by product revision, failing DUT steps versus spec limits, measurement-trace summaries, and calibration-due assets.

This is the fleet/data-layer companion to daq-mcp. daq-mcp talks to a single DAQ device. systemlink-mcp queries and correlates results, assets, and systems across a lab.

This is a personal project with one maintainer. It is not an NI product and is not affiliated with, endorsed by, or supported by NI / Emerson.

Why test-domain tools instead of a generic wrapper

A commercial middleware catalog can list SystemLink among thousands of industrial systems and then expose a CMMS/ERP-shaped surface: work orders, sites, spaces, sales orders. That abstraction cannot answer what a test engineer actually asks:

  • pass/fail rates for product revision B this quarter
  • which measurement on a failing DUT exceeded its limit
  • which PXI module is past its calibration due date

Every tool here is shaped around those questions. Responses are summaries plus a bounded preview, not raw Test Monitor or DataFrame payloads. Dumping thousands of result rows or a 2000-point waveform into a model context makes the server unusable even if the API calls are correct.

Read-only by default

Mutating tools (update_result_properties, execute_notebook) refuse unless SYSTEMLINK_MCP_ALLOW_WRITE=1. Read tools work with no extra flags.

The current nisystemlink-clients AssetManagementClient has create, query, delete, file-link, and utilization methods, but no asset update method. The gated write surface therefore updates Test Monitor result metadata and queues notebook executions, not asset records.

Stack

Python 3.11+, uv, FastMCP (stdio or Streamable HTTP), Pydantic v2, and the official nisystemlink-clients package. Tools never import the SDK; they call a SystemLinkBackend. Set SYSTEMLINK_MCP_SIMULATE=1 for a pure-Python fake that needs no SystemLink server.

Quick start (no SystemLink server)

uv sync
SYSTEMLINK_MCP_SIMULATE=1 uv run server.py

PowerShell:

$env:SYSTEMLINK_MCP_SIMULATE="1"; uv run server.py

Real SystemLink

The simulated backend is only used when SYSTEMLINK_MCP_SIMULATE=1, or when a live connect fails and SYSTEMLINK_MCP_REQUIRE_REAL is unset. A live response has "backend": "systemlink" and "simulated": false.

  1. Create an API key in SystemLink (Enterprise: a policy, then an API key; the HTTP header the SDK sends is x-ni-api-key). SLE does not accept username and password for programmatic access.
  2. Copy .env.example to .env (gitignored) and set:
SYSTEMLINK_SERVER_URI=https://your-systemlink-host
SYSTEMLINK_API_KEY=...
SYSTEMLINK_MCP_REQUIRE_REAL=1

Leave SYSTEMLINK_MCP_SIMULATE unset. On-prem SystemLink Server that still allows basic auth can use SYSTEMLINK_USERNAME / SYSTEMLINK_PASSWORD instead of a key. SystemLink Cloud can use the API key alone (no URI); the backend then constructs CloudHttpConfiguration.

  1. Probe before MCP so a failed login cannot silently become the fake fleet:
uv run server.py --probe

You should see "simulated": false and a short query_systems preview from your server. Then launch MCP without the simulate flag, or point Cursor at .cursor/mcp.json.live.example.

If --probe fails, the JSON error is from the SDK (ApiException / connect), not from dummy data.

Tools

Tool Access What it answers
summarize_yield read Pass/fail yield, optionally grouped by part, program, or serial
query_results read Filtered result counts plus a bounded recent preview
get_failing_steps read Failing steps for one result, with measurement vs limits
summarize_measurement read Table stats plus a downsampled trace (decimated on the live API)
list_calibration_due read Assets past or approaching calibration due
query_assets read Inventory with presence and calibration status
query_systems read Registered systems and connection health
query_products read Products / part numbers / families
query_specs read Spec limits for a product
list_files read File metadata for a result or asset (no contents)
update_result_properties gated write Keywords/properties on a test result
execute_notebook gated write Queue a Jupyter notebook execution

MCP Inspector

From the repo root, with simulation forced:

SYSTEMLINK_MCP_SIMULATE=1 npx -y @modelcontextprotocol/inspector uv run server.py

PowerShell:

$env:SYSTEMLINK_MCP_SIMULATE="1"
npx -y @modelcontextprotocol/inspector uv run server.py

Open the printed URL (typically http://127.0.0.1:6274). Confirm the twelve tools appear, then call summarize_yield with group_by=part_number.

Streamable HTTP instead of stdio:

SYSTEMLINK_MCP_SIMULATE=1 uv run server.py --http

The MCP endpoint is http://127.0.0.1:8000/mcp. Do not use transport="sse"; that transport is deprecated.

Cursor

Use .cursor/mcp.json.example (simulator) or .cursor/mcp.json.live.example (real server). Machine-local .cursor/mcp.json is gitignored.

{
  "mcpServers": {
    "systemlink-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "C:/Users/folayaod/personal/systemlink-mcp",
        "server.py"
      ],
      "env": {
        "SYSTEMLINK_MCP_SIMULATE": "1"
      }
    }
  }
}

Claude Desktop

Same JSON block, in claude_desktop_config.json:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Restart Claude Desktop after saving.

Prompts that exercise the simulated fleet

  1. "What is the pass/fail yield for part number PN-5164-B, grouped against the other revisions?"
  2. "DUT-1001 failed BoardFunctional.seq. Which step failed, and how did Gain compare to its limits? Summarize the attached measurement trace."
  3. "Which PXI assets are overdue or approaching calibration, and is PXI Rack 1 connected?"

Tests

SYSTEMLINK_MCP_SIMULATE=1 uv run pytest

Tests hit the simulated backend only. They do not open a network connection to SystemLink.

Status

Personal project, one maintainer. The simulated backend is the supported day-to-day path. The real backend is written against nisystemlink-clients 2.32.x APIs documented at python-docs.systemlink.io and the ni/nisystemlink-clients-python source. It has not been run against a live SystemLink Enterprise instance in this repository's CI. Asset Dynamic LINQ property names for calibration status follow the PascalCase style of the official AssetIdentifier example; if a live server rejects that filter, that is a known integration risk documented in DEVLOG.md.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选