Can my agent retrieve real-time metrics from an active ML run?

Yes. Use the 'get_experiment_metrics' tool with the experiment key. The agent will pull the latest numeric logged endpoints, allowing you to monitor loss, accuracy, and other custom metrics as they are generated.

How do I audit the parameters used in a specific experiment?

Provide the experiment key to your agent. The 'get_experiment_params' tool extracts all logged ML properties, helping you verify hyperparameters like learning rates, batch sizes, and model architectures.

Can I see a list of all experiments within a specific project?

Absolutely. Use the 'list_experiments' tool with the project ID. Your agent will surface all ML runs within that project, including their status and metadata, so you can quickly identify the results you need.

Comet ML MCP Server for LlamaIndex 6 tools — connect in under 2 minutes

Name: Comet ML
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Comet ML as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Comet ML. "
            "You have 6 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Comet ML?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Comet ML MCP Server

Connect your Comet ML account to any AI agent and take full control of your machine learning lifecycle through natural conversation.

LlamaIndex agents combine Comet ML tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Experiment Tracking — List and audit machine learning runs to inspect performance metadata, tags, and live execution statuses
Numeric Metric Auditing — Retrieve high-precision numeric endpoints mapping metrics generated dynamically during your training loops
Parameter Inspection — Extract explicit ML properties like learning rates and configurations logged to specific experiment keys
Project & Workspace Navigation — Navigate through organizational namespaces and identify exactly where your ML research resides
Run Metadata Analysis — Discovered disconnected physical limits parsing explicit run structures, timing, and structural configurations

The Comet ML MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Comet ML to LlamaIndex via MCP

Follow these steps to integrate the Comet ML MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Comet ML

Why Use LlamaIndex with the Comet ML MCP Server

LlamaIndex provides unique advantages when paired with Comet ML through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Comet ML tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Comet ML tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Comet ML, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Comet ML tools were called, what data was returned, and how it influenced the final answer

Comet ML + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Comet ML MCP Server delivers measurable value.

Hybrid search: combine Comet ML real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Comet ML to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Comet ML for fresh data

Analytical workflows: chain Comet ML queries with LlamaIndex's data connectors to build multi-source analytical reports

Comet ML MCP Tools for LlamaIndex (6)

These 6 tools become available when you connect Comet ML to LlamaIndex via MCP:

get_experiment

Retrieve explicit Cloud logging tracing explicit Payload IDs

get_experiment_metrics

Execute static mapping targeting exactly defined numeric bounds natively

get_experiment_params

Inspect internal properties detailing API taxonomy types

list_experiments

Discover explicit routing arrays structuring specific logged experiment limits

list_projects

Perform structural extraction matching target Projects inside Comet

list_workspaces

Identify bounded routing spaces inside the Headless Comet ML limits

Example Prompts for Comet ML in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Comet ML immediately.

"List all projects in workspace 'research-team'"

"Get current metrics for experiment 'exp_abc123'"

"What hyperparameters were used in experiment 'exp_789'?"

Troubleshooting Comet ML MCP Server with LlamaIndex

Common issues when connecting Comet ML to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Comet ML + LlamaIndex FAQ

Common questions about integrating Comet ML MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Comet ML tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.