AgentOps MCP for AI. Monitor agent performance, costs, and failure points.
Works with every AI agent you already use
…and any MCP-compatible client








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.
Retrieves high-level status information for the entire AgentOps monitoring project.
Inspects complete agent runs (traces) to map out decision flow and sequence of events.
Provides detailed metrics for a specific trace, including token usage counts and associated monetary cost.
Drills down into single spans within a trace to examine tool inputs or LLM interactions precisely.
Ask an AI about this
Waiting for input…
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 VinkiusGet 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.
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
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
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
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.
019e5cf8-2783-7027-acbb-dd1460010e3a Here's how it actually works
The bottom line is you get a clean, structured stream of operational data about your agents' behavior.
First, subscribe to this MCP and input your AgentOps API key.
Next, run an agent workflow that generates telemetry data you want to monitor.
Finally, use the relevant tool—for instance, running get_trace—to pull structured metrics detailing performance and costs.
Who is this actually for?
This MCP targets the engineering team responsible for production AI systems. Specifically, it’s for the ML Engineer who needs to debug why an agent failed on a Tuesday afternoon, or the Product Manager worried about uncontrolled API spend.
Debugging complex multi-step agent loops and pinpointing exact failure points or bottlenecks.
Ensuring production AI systems maintain reliability by collecting structured telemetry data for monitoring dashboards.
Monitoring token costs and usage patterns across agent deployments to prove ROI and manage budgets.
What Changes When You Connect
Pinpoint failures instantly. When an agent fails, you use get_trace to see the full sequence of calls, knowing exactly where the logic broke down.
Control your budget. Use get_trace_metrics before deployment to estimate and track token consumption for specific workflows.
Debug deep issues. If a step is slow, don't guess. Use get_span to zoom in on that single interaction and see the raw input/output parameters.
Understand scope. Before writing code, run get_project to confirm which monitoring project you are currently connected to.
Maintain reliability. By collecting structured data via this MCP, your agents become auditable components of your larger system.
See it in action
Agent fails on external tool call
A user asks the agent to search for a flight, but it fails. Instead of just seeing 'Error,' you use get_trace to see that the failure occurred in the final span and that the input parameters sent were malformed.
Unexpectedly high API costs
The agent works great, but the bill is too high. You run get_trace_metrics on a sample successful run to determine if 80% of your cost comes from prompt tokens or completion tokens.
Debugging multi-step reasoning
The agent's answer is wrong, but you can't tell why. You use get_span to examine the intermediate steps—the raw decision points—to see where the model misinterpreted the initial data.
The honest tradeoffs
Assuming local logging is enough
You just dump print statements in your code. When things break, you only get a stack trace saying 'Error' but no context on why the state was wrong.
Use get_trace to capture structured telemetry data automatically. It records every input and output for debugging, so you don't rely on local print statements.
Ignoring cost over time
The agent runs fine in staging, but the API bill skyrockets after launch. You realize nothing was tracked regarding token usage per run.
Use get_trace_metrics to monitor costs immediately. This gives you a hard number on token consumption before deployment.
Debugging complex failures
The agent calls three different tools and then fails. You can only see the final error message, making it impossible to know which of the three was responsible.
Use get_span in conjunction with get_trace to isolate the exact segment (the span) that failed, regardless of how many other steps preceded it.
When It Fits, When It Doesn't
You need this MCP if your agent's success depends on reliable execution paths and cost control. If you only need a simple 'on/off' switch for an action, don't use this; that requires basic function calling. However, if your agents perform multi-step reasoning or interact with multiple external services, you must use get_trace and get_span. If predicting costs is the main concern, focus solely on get_project and get_trace_metrics. Don't use this if you aren't tracking data; use a simple logging service instead.
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.
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 gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.