Vinkius
AgentOps

AgentOps MCP for AI. Monitor agent performance, costs, and failure points.

Claude Claude
ChatGPT ChatGPT
Cursor Cursor
Gemini Gemini
Windsurf Windsurf
VS Code VS Code
JetBrains JetBrains
Vercel Vercel
See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

AgentOps (Agent Telemetry and Monitoring) MCP on Cursor AI Code EditorAgentOps (Agent Telemetry and Monitoring) MCP on Claude Desktop AppAgentOps (Agent Telemetry and Monitoring) MCP on OpenAI Agents SDKAgentOps (Agent Telemetry and Monitoring) MCP on Visual Studio CodeAgentOps (Agent Telemetry and Monitoring) MCP on GitHub Copilot AI AgentAgentOps (Agent Telemetry and Monitoring) MCP on Google Gemini AIAgentOps (Agent Telemetry and Monitoring) MCP on Lovable AI DevelopmentAgentOps (Agent Telemetry and Monitoring) MCP on Mistral AI AgentsAgentOps (Agent Telemetry and Monitoring) MCP on Amazon AWS Bedrock

Connect to your AI in seconds.

AgentOps tracks performance data for your AI agents. It lets you inspect how agentic workflows perform by retrieving execution traces, analyzing specific spans, and monitoring token costs in real time.

What your AI can do

Get trace

Retrieves the complete record and sequence for a specific, full agent run.

Get project

Retrieves basic configuration details for the AgentOps monitoring project.

Get span

Gets detailed information about a single segment of an agent's execution path.

+ 1 more capabilities included
Review project metadata

Retrieves high-level status information for the entire AgentOps monitoring project.

Analyze full execution paths

Inspects complete agent runs (traces) to map out decision flow and sequence of events.

Calculate performance costs

Provides detailed metrics for a specific trace, including token usage counts and associated monetary cost.

Debug individual steps

Drills down into single spans within a trace to examine tool inputs or LLM interactions precisely.

Included with Plan

Waiting for input…

AI Agent

AgentOps (Agent Telemetry and Monitoring) - 4 Tools

These four tools let you audit an AI agent's performance by retrieving project metadata, viewing full run traces, calculating costs, or inspecting individual execution spans.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using AgentOps (Agent Telemetry and Monitoring) on Vinkius

Get Trace

Retrieves the complete record and sequence for a specific, full agent run.

Get Project

Retrieves basic configuration details for the AgentOps monitoring project.

Get Span

Gets detailed information about a single segment of an agent's execution path.

Get Trace Metrics

Calculates metrics like token count and cost associated with a specific recorded...

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The AgentOps integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on every call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with AgentOps (Agent Telemetry and Monitoring), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,100+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Every connection is secured and compliant automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog every week
AgentOps MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AgentOps. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 4 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Debugging an Agent's Logic Flow

Today, when an agent gives a bad answer, the process is painful. You have to manually sift through console logs and fragmented error messages to figure out which of the dozens of steps went wrong. Was it the tool call? Did the model misunderstand the input from the previous step? It takes hours just to reconstruct the sequence.

With this MCP, you get a complete audit trail. By calling `get_trace`, your agent’s entire thought process is mapped out in one place. You see every decision point and every tool interaction recorded cleanly. You don't guess; you read the evidence.

Using AgentOps to Isolate Performance Issues

Previously, if an agent was slow, you had no idea what part of that sequence was bogging it down. You couldn't tell if the delay was in the LLM call itself or if a specific external API took too long to respond. Everything looked lumped together.

Now, by inspecting individual spans using `get_span`, you isolate the problem perfectly. That slow tool call? There's the data, starting and ending times, right there. You know exactly what needs fixing.

What your AI can actually do with this

When building complex AI agents, knowing what's happening under the hood is critical. This MCP gives you deep visibility into the entire lifecycle of an agent run. Instead of just getting a final answer, your agent provides data detailing every step it took, including which tools it called and how much compute power that cost.

You can inspect specific execution traces to understand decision points or find where slowdowns happen. It also tracks token usage across all interactions, helping you manage costs before they get out of hand. Connecting this MCP via Vinkius allows your agent to report its operational metrics directly through any compatible client.

Built · Hosted · Managed by Vinkius AgentOps - Monitor AI Agent Traces and Costs
Server ID 019e5cf8-2783-7027-acbb-dd1460010e3a
Vinkius Inspector
Compliance Grade A+
Score 98.33/100
Vinkius Inspector Badge — Score 98.33/100

Questions you might have

How do I check my agent's current monitoring project details using get_project? +

You call get_project to retrieve the high-level metadata for your AgentOps setup. This confirms which environment and API key are active, ensuring you’re looking at the correct data set.

What is the difference between get_trace and get_span? +

A trace (get_trace) gives you the full picture of an entire run. A span (get_span) lets you drill down into one small, specific segment within that larger run for deep inspection.

Can I track token usage with get_trace_metrics? +

Yes, get_trace_metrics provides detailed data on token counts for both prompt and completion segments. This lets you calculate the exact cost associated with any given workflow.

Is AgentOps good for multi-agent systems? +

Absolutely. The MCP is designed to capture complex, sequential interactions common in multi-agent setups, allowing you to monitor how agents pass information between each other.

When debugging a workflow failure, what specific information can I get using get_span? +

get_span gives you granular details about one specific action within the agent's run. You see exactly what parameters were used for tool calls or LLM interactions, and precisely when that step started and finished. This helps pinpoint whether a failure came from bad input data or an unexpected external service response.

How do I check my AgentOps project status using get_project before running complex jobs? +

You use get_project to verify your connection and retrieve high-level details about your current monitoring setup. This ensures your API key is linked to the correct, active project ID. It's a quick way to confirm you're looking at the right data source before deep debugging.

Are there limitations on how much data I can pull with get_trace for very long workflows? +

While we handle large datasets, repeatedly running get_trace on massive traces might hit rate limits. For the best performance when analyzing complex runs, it's better to first use get_project to scope your search, or break down the investigation using smaller, targeted calls with get_span.

Beyond token costs, what specific timing metrics does get_trace_metrics provide? +

get_trace_metrics provides critical time-based data. You can see the total execution duration for a trace, as well as how long individual spans took to complete. This lets you determine if latency is caused by the model itself or by external API calls.

How can I check the token usage and cost for a specific agent execution? +

You can use the get_trace_metrics tool by providing the specific Trace ID. It will return detailed data on token counts and the calculated financial cost for that execution.

Can I see the details of a single step within a larger trace? +

Yes! Use the get_span tool with the specific Span ID. This allows you to isolate and inspect individual operations, such as a single tool call or a specific LLM completion.

How do I verify which AgentOps project is currently active? +

Simply run the get_project tool. It retrieves the metadata and configuration details of the project associated with your current API key.

Built & Managed by Vinkius 30s setup 4 tools

We've already built the connector for AgentOps. Just plug in your AI agents and start using Vinkius.

No hosting. No infrastructure. No complex setup.
All 4 tools are live and waiting. You're up and running in seconds.

Vinkius runs on Claude Claude
Vinkius runs on ChatGPT ChatGPT
Vinkius runs on Cursor Cursor
Vinkius runs on Gemini Gemini
Vinkius runs on Windsurf Windsurf
Vinkius runs on VS Code VS Code
Vinkius runs on JetBrains JetBrains
Vinkius runs on Vercel Vercel
+ other MCP clients

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