systemlink-mcp
Enables queuing Jupyter notebook executions for automated analysis and reporting on test results.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@systemlink-mcp Summarize yield for product revision B this quarter."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: Auto-Manager MCP
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.pyPowerShell:
$env:SYSTEMLINK_MCP_SIMULATE="1"; uv run server.pyReal 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.
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.Copy
.env.exampleto.env(gitignored) and set:
SYSTEMLINK_SERVER_URI=https://your-systemlink-host
SYSTEMLINK_API_KEY=...
SYSTEMLINK_MCP_REQUIRE_REAL=1Leave 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.
Probe before MCP so a failed login cannot silently become the fake fleet:
uv run server.py --probeYou 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 |
| read | Pass/fail yield, optionally grouped by part, program, or serial |
| read | Filtered result counts plus a bounded recent preview |
| read | Failing steps for one result, with measurement vs limits |
| read | Table stats plus a downsampled trace (decimated on the live API) |
| read | Assets past or approaching calibration due |
| read | Inventory with presence and calibration status |
| read | Registered systems and connection health |
| read | Products / part numbers / families |
| read | Spec limits for a product |
| read | File metadata for a result or asset (no contents) |
| gated write | Keywords/properties on a test result |
| 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.pyPowerShell:
$env:SYSTEMLINK_MCP_SIMULATE="1"
npx -y @modelcontextprotocol/inspector uv run server.pyOpen 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 --httpThe 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.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Restart Claude Desktop after saving.
Prompts that exercise the simulated fleet
"What is the pass/fail yield for part number PN-5164-B, grouped against the other revisions?"
"DUT-1001 failed BoardFunctional.seq. Which step failed, and how did Gain compare to its limits? Summarize the attached measurement trace."
"Which PXI assets are overdue or approaching calibration, and is PXI Rack 1 connected?"
Tests
SYSTEMLINK_MCP_SIMULATE=1 uv run pytestTests 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.
Available Tools
12 toolsexecute_notebookA
Start a SystemLink Jupyter notebook execution. Requires SYSTEMLINK_MCP_ALLOW_WRITE=1.
Uses NotebookClient.create_executions. This queues work on the server; it does not wait for completion or return notebook output.
notebook_id: Notebook id to run. workspace_id: Workspace the execution belongs to. parameters: Optional notebook parameters (values are stringified for the SDK).
Inputs: notebook_id (required), workspace_id (required), parameters. Returns: object
get_failing_stepsA
Return failing steps for one test result, with measurements versus limits.
This is the DUT-debug view: which step failed and how the measurement compared to the spec window. Step payloads are summarized; only failing steps are previewed.
result_id: Test Monitor result id. preview_limit: Max failing steps to include (capped at 50).
Inputs: result_id (required), preview_limit. Returns: object
list_calibration_dueA
List instruments whose calibration is overdue or approaching due.
This is the PXI-fleet question a generic CMMS wrapper cannot ask: which chassis or module needs cal, and when. Default statuses are PAST_RECOMMENDED_DUE_DATE and APPROACHING_RECOMMENDED_DUE_DATE.
include_out_for_calibration: Also include OUT_FOR_CALIBRATION assets. preview_limit: Max assets to include (capped at 50).
Inputs: preview_limit, include_out_for_calibration. Returns: object
list_filesA
List file metadata attached to a result or asset. Does not download contents.
file_ids: Explicit file ids. result_id: Test result whose file_ids should be listed. asset_id: Asset whose linked files should be listed. preview_limit: Max files to include (capped at 50).
Inputs: asset_id, file_ids, result_id, preview_limit. Returns: object
query_assetsA
Query the asset inventory: model, serial, presence, and calibration state.
name: Substring match on asset name. serial_number: Exact serial. model_name: Substring match on model, e.g. PXIe-6368. calibration_status: OK, APPROACHING_RECOMMENDED_DUE_DATE, PAST_RECOMMENDED_DUE_DATE, or OUT_FOR_CALIBRATION. preview_limit: Max assets to include (capped at 50).
Inputs: name, model_name, preview_limit, serial_number, +1 more. Returns: object
query_productsB
Query products and part numbers used to correlate test results by revision.
family: Product family, e.g. Oscilloscopes. part_number: Exact part number / revision. name: Exact product name. preview_limit: Max products to include (capped at 50).
Inputs: name, family, part_number, preview_limit. Returns: object
query_resultsA
Query test results and return counts plus a bounded recent preview.
Does not dump the full result set. Counts use Test Monitor return_count; the preview is the newest matching results only.
part_number: Product revision / part number. program_name: Test program or sequence name. serial_number: DUT serial. status: Optional status.statusType filter (PASSED, FAILED, ERRORED, ...). started_after: Inclusive ISO-8601 UTC start. started_before: Inclusive ISO-8601 UTC end. preview_limit: Max result rows to include (capped at 50).
Inputs: status, part_number, program_name, preview_limit, +3 more. Returns: object
query_specsA
Query specification limits for a product (min / typical / max, unit).
product_id: Product id or, in simulation, the part number used as product_id. preview_limit: Max specs to include (capped at 50).
Inputs: product_id (required), preview_limit. Returns: object
query_systemsA
Query registered test systems and their connection health.
alias: System alias or id substring. connected: If set, filter on status.data.http_connected. preview_limit: Max systems to include (capped at 50).
Inputs: alias, connected, preview_limit. Returns: object
summarize_measurementB
Summarize a measurement table: stats plus a downsampled trace preview.
On a live server this uses DataFrameClient.query_decimated_data rather than downloading every row. Pass table_id directly, or result_id to use the first attached data table.
table_id: DataFrame table id. result_id: Test result whose data_table_ids should be used if table_id is omitted. preview_limit: Max preview points (capped at 100).
Inputs: table_id, result_id, preview_limit. Returns: object
summarize_yieldA
Summarize pass/fail yield for test results.
Uses Test Monitor result-level status, not CMMS work-order counts. Filters are pushed to the server as Dynamic LINQ (partNumber, programName, serialNumber, status.statusType, startedAt). Yield is passed / (passed + failed).
part_number: Product revision / part number, e.g. PN-5164-B. program_name: Test program or sequence name, e.g. BoardFunctional.seq. serial_number: DUT serial. status: Optional status.statusType filter (PASSED, FAILED, ERRORED, ...). started_after: Inclusive ISO-8601 UTC start, e.g. 2026-01-01T00:00:00Z. started_before: Inclusive ISO-8601 UTC end. group_by: Break yield down by part_number, program_name, serial_number, or none.
Inputs: status, group_by, part_number, program_name, +3 more. Returns: object
update_result_propertiesA
Update keywords or properties on a test result. Requires SYSTEMLINK_MCP_ALLOW_WRITE=1.
Uses TestMonitorClient.update_result. Asset metadata cannot be patched through the current AssetManagementClient, which has no update method.
result_id: Test Monitor result id. keywords: Replacement keyword list if provided. properties: Properties to merge onto the result.
Inputs: result_id (required), keywords, properties. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 12 tools
Each tool targets a distinct resource and action: yield aggregation, result queries, step-level failure analysis, measurement statistics, calibration status, asset/system/product/spec queries, file listing, result property updates, and notebook execution. There is no overlap; even query_results and summarize_yield differ clearly (raw counts vs pass/fail ratio). Descriptions explicitly disambiguate boundaries.
All tool names follow a consistent verb_noun pattern in snake_case: summarize_yield, query_results, get_failing_steps, list_calibration_due, execute_notebook, etc. Retrieval verbs vary (query vs list vs get) but are semantically appropriate and predictable, with no mixing of conventions.
Twelve tools is well within the ideal 3–15 range and appropriately scoped for a SystemLink MCP server covering test data, asset management, and notebook execution. Each tool adds distinct value; none feel redundant or superfluous.
The surface covers the core read workflows (yield, results, failing steps, measurements, assets, systems, products, specs, files) plus limited write actions (update_result_properties, execute_notebook). Notable gaps include lack of update/delete for assets (acknowledged as unsupported by the underlying client), no single-result full detail view beyond failing steps, and no creation of test results. However, these are minor for the apparent purpose of monitoring and analysis.
Maintenance
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