Skip to main content
Glama
rhit-folayaod

systemlink-mcp

by rhit-folayaod
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to systemlink-mcp

No user-submitted related servers found.

    Related Servers

    • AgentKit

      Yacine-ai-tech
      A
      license
      Not graded
      quality
      B
      maintenance
      Exposes enterprise KPIs, health scores, forecasting, and anomaly detection as MCP tools, resources, and prompts for use by any MCP-compatible agent.
      AGPL 3.0

    • Auto-Manager MCP

      Maxpeng59
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables natural language analysis of mechanical test data files (CSV, TDMS, MDF) by providing tools for channel statistics, spectrum analysis, rainflow fatigue counting, thermal state detection, and report generation.
      MIT

    • digital-twin-mcp

      xici001
      A
      license
      A
      quality
      C
      maintenance
      Exposes digital twin device data capabilities as MCP tools, allowing MCP clients to query device status, read real-time metrics, fetch time-series data, and check alerts. Includes a simulated PLC driver with a clean interface for connecting real devices via OPC-UA, Modbus, or gateway APIs.
      6
      MIT

    • motor-telemetry-mcp

      0xcf02
      A
      license
      Not graded
      quality
      C
      maintenance
      Exposes industrial motor telemetry data to AI agents via MCP and REST, enabling natural-language queries about motor status, health, and alerts. Provides a single tool and resource for retrieving real-time motor metrics and escalating critical conditions.
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.1/5.0

    Scored across 12 tools

    Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count5/5

    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.

    Completeness4/5

    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

    ActivityMaintained
    ResponsivenessNo issues

    Resources