phoenix-mcp-eval

MCP server for Arize Phoenix — LLM tracing, evaluation, and dataset management via AI agents.

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What is this?

phoenix-mcp-eval is an MCP (Model Context Protocol) server that exposes Arize Phoenix's LLM observability capabilities to AI agents. It enables AI-driven analysis of traces, evaluation of LLM outputs, and management of evaluation datasets — directly from an MCP-compatible agent.

Built for platform engineers and ML teams running LLM pipelines on AI Foundry, LangChain, or LlamaIndex who need automated quality assurance and tracing.

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Available Tools

Tool	Description
list_projects	List all Phoenix tracing projects
get_traces	Retrieve LLM traces for a project with filters
get_spans	Get individual spans with input/output/latency data
list_datasets	List evaluation datasets in Phoenix
get_dataset	Fetch dataset examples for review or comparison
list_evaluations	List evaluation runs and their scores
get_evaluation_summary	Get aggregated evaluation metrics (precision, recall, etc.)
query_traces	Run structured queries over trace data

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Quick Start

Prerequisites

• Python 3.11+
• Arize Phoenix instance (self-hosted or cloud)
• Phoenix API key or local server URL

Installation

git clone https://github.com/akkireddy-challa/phoenix-mcp-eval
cd phoenix-mcp-eval
pip install -r requirements.txt

Configuration

export PHOENIX_HOST=http://localhost:6006
export PHOENIX_API_KEY=<your-api-key>  # if using cloud

Run

python server.py

MCP Client Config (Claude Desktop)

{
  "mcpServers": {
    "phoenix": {
      "command": "python",
      "args": ["/path/to/phoenix-mcp-eval/server.py"],
      "env": {
        "PHOENIX_HOST": "http://localhost:6006"
      }
    }
  }
}

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Security Model

• Connects to Phoenix via API key or local network only
• All operations are read-only by default (trace/eval retrieval)
• No model weights, prompts, or PII are transmitted outside Phoenix
• API key stored in environment variables, never in code
• Designed for internal network use within a Kubernetes cluster

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Use Cases at Telia

This pattern is used to allow AI agents to:

• Automatically review LLM trace quality after AI Foundry deployments
• Surface failing evaluation metrics to on-call engineers without manual Phoenix access
• Compare evaluation datasets across model versions
• Trigger re-evaluation jobs based on trace anomaly detection

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Roadmap

• [ ] run_evaluation — trigger evaluation jobs programmatically
• [ ] create_dataset — export traces to evaluation datasets
• [ ] get_prompt_templates — retrieve versioned prompts from Phoenix
• [ ] Integration with Azure AI Foundry deployment events
• [ ] GitHub Actions workflow for CI validation

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Related Projects

Repo	Purpose
k8s-mcp-server	Kubernetes cluster diagnostics via MCP
azure-mcp-platform	Azure resource management via MCP
grafana-mcp-observability	Grafana dashboards and alerts via MCP

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License

MIT License. See LICENSE for details.