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Repository Install

MCP Server for cloud-bulldozer/orion

概要

Orion MCP is a Model Context Protocol (MCP) server for performance regression analysis powered by the cloud-bulldozer/orion library. * – Automatically detects performance regressions in OpenShift & Kubernetes clusters. * – Exposes a set of composable tools & resources that can be consumed via HTTP or by other MCP agents. * – Generates publication-ready plots (PNG/JPEG) for trends, multi-version comparisons and metric correlations. * – Ships with a lightweight OCI image and an example OpenShift deployment manifest. * 3.11 or newer * An (or Elasticsearch ≥7.17) endpoint with Orion-indexed benchmark results * or (optional – for containerised execution) Set the data-source endpoint and launch the server locally: The openshift_report_on_pr tool provides for GitHub Pull Requests. This feature compares the performance metrics of a specific PR against the periodic baseline performance to identify potential regressions. 1.

README

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Orion MCP


Orion MCP is a Model Context Protocol (MCP) server for performance regression analysis powered by the cloud-bulldozer/orion library.

Key Features

  • Regression Detection – Automatically detects performance regressions in OpenShift & Kubernetes clusters.
  • Interactive MCP API – Exposes a set of composable tools & resources that can be consumed via HTTP or by other MCP agents.
  • Visual Reporting – Generates publication-ready plots (PNG/JPEG) for trends, multi-version comparisons and metric correlations.
  • Container-first – Ships with a lightweight OCI image and an example OpenShift deployment manifest.

Table of Contents

  1. Getting Started
  2. Quick Start
  3. Available Tools
  4. Deployment
  5. Development
  6. Contributing
  7. License

Getting Started

Prerequisites

  • Python 3.11 or newer
  • An OpenSearch (or Elasticsearch ≥7.17) endpoint with Orion-indexed benchmark results
  • Podman or Docker (optional – for containerised execution)

Installation (virtual-env)

# Clone repository
$ git clone https://github.com/YOUR_ORG/orion-mcp.git && cd orion-mcp

# Create & activate a virtual environment
$ python3.11 -m venv .venv
$ source .venv/bin/activate

# Install Python dependencies
$ pip install -r requirements.txt

Quick Start

Set the data-source endpoint and launch the server locally:

export ES_SERVER="https://opensearch.example.com:9200"
python orion_mcp.py  # listens on 0.0.0.0:3030 by default

Available Tools

Tool Description Default Arguments
get_data_source Returns the configured OpenSearch URL none
get_orion_configs Lists available Orion configuration files none
get_orion_metrics Lists metrics grouped by Orion config config_name="small-scale-udn-l3.yaml", version="4.20"
get_orion_metrics_with_meta Lists metrics plus metadata for Orion config config_name="small-scale-udn-l3.yaml", version="4.19"
get_orion_performance_data Returns raw performance values for config/metric/version config_name="small-scale-udn-l3.yaml", metric="podReadyLatency_P99", version="4.19", lookback="15"
openshift_report_on Generates a trend line for one or more OCP versions versions="4.19", lookback="15", metric="podReadyLatency_P99", config_name="small-scale-udn-l3.yaml"
openshift_report_on_pr NEW Analyzes performance impact of a specific Pull Request version="4.20", lookback="15", organization="openshift", repository="ovn-kubernetes", pull_request="2841"
GitHub で README 全文を見る

インストール

This server does not publish a one-line install command.

Open the repository installation guide MCP 初心者？mcpskill.org のガイドから始めましょう →