AgentOps MCP. See exactly where your agent fails or costs too much.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
AgentOps (Agent Telemetry and Monitoring) tracks the performance of your AI agents. It lets you view project status, inspect full execution traces, and monitor costs by calling specific tools.
You can pinpoint exactly where an agent slows down or runs out of tokens. It's for debugging complex agent workflows and keeping an eye on operational costs.
What your AI agents can do
Get project
Retrieves the current high-level details for the AgentOps project.
Get span
Retrieves deep details about a specific, isolated segment of an agent's execution.
Get trace
Retrieves the full sequence of events and decision-making for a specific agent run.
Use get_project to pull core details about the AgentOps project associated with your API key.
Use get_trace to get the full sequence of events and decision points for a specific agent run.
Use get_span to examine the precise start and end points of individual tool calls or LLM interactions within a trace.
Use get_trace_metrics to get quantitative data on a trace, including total token count and estimated cost.
Ask AI about this MCP
Supported MCP Clients
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019e5cf8get project
Retrieves the current high-level details for the AgentOps project.
019e5cf8get span
Retrieves deep details about a specific, isolated segment of an agent's execution.
019e5cf8get trace
Retrieves the full sequence of events and decision-making for a specific agent run.
019e5cf8get trace metrics
Calculates key metrics like token count and associated cost for a specific trace ID.
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 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ 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
What you can do with this MCP connector
AgentOps keeps tabs on your AI agents. It lets you see exactly how they're running, where they're hitting snags, and what they're costing you. You use it to debug complex agent workflows and keep a tight grip on operational costs.
To start, you can use get_project to pull the main details for the AgentOps project tied to your API key. You'll get the high-level status right off the bat. To understand how an agent actually behaved, you run get_trace. This pulls the full sequence of events and all the decision points for any specific agent run.
If you need to zoom in, get_span lets you check the precise start and end points for individual tool calls or LLM interactions within that trace. You can then run get_trace_metrics to get the numbers: it calculates the total token count and the associated cost for any trace ID you give it.
How AgentOps MCP Works
- 1 First, subscribe to the AgentOps server and provide your API key.
- 2 Second, connect your preferred AI client (Claude, Cursor, etc.) to the MCP server.
- 3 Third, run a tool call (e.g.,
get_trace) to start monitoring your agent's performance data.
The bottom line is that you get a structured view of your agent's internal operations, allowing you to track performance and cost metrics.
Who Is AgentOps MCP For?
This is for AI Engineers and DevOps teams. If you're debugging complex agent loops or managing the cost of production AI agents, you need this. It gives you the visibility to move beyond 'it worked last time' and actually know why it failed, or why it cost too much.
Debugging complex agentic loops. They use get_span and get_trace to pinpoint exactly where a process slowed down or failed.
Ensuring the reliability of production AI agents. They use get_project and get_trace_metrics to confirm observability and manage deployment performance.
Monitoring token costs and usage patterns across multiple agent deployments to justify ROI and set budget limits.
What Changes When You Connect
- Pinpoint Failure Locations: Instead of guessing, you get precise data. Using
get_span, you drill down to the exact tool call or LLM interaction that caused a failure or slowdown. - Control Operating Costs: Monitor agent spending directly.
get_trace_metricsprovides token usage and cost estimates for every run, helping you optimize the entire system. - Understand the Full Narrative: The
get_tracetool reconstructs the entire agent journey. You see the full decision path—how the agent moved from prompt to result. - Verify Project Scope:
get_projectgives you immediate confirmation of the current project ID and key details, making sure you're tracking the right agent deployment. - Deep Debugging: Combining
get_traceandget_spanlets you track the sequence of events, understanding why an agent made a particular choice at a specific moment in time.
Real-World Use Cases
Debugging a Failing Agent Loop
An AI Engineer notices an agent fails intermittently. They run get_trace to capture the failure sequence. They then use get_span on the last few spans to see if the failure happened during a specific tool call or an LLM generation step, finally confirming the bad input parameter.
Budgeting for a New Product Feature
A Product Manager needs to estimate the cost of a new agent feature. They run a test case and use get_trace_metrics to calculate the total token usage and estimated cost. This data informs the final product pricing and budget.
Auditing Production Performance
A DevOps team needs to prove the reliability of a live agent. They run get_project to verify the current deployment scope, then use get_trace and get_trace_metrics on several samples to prove stable performance metrics.
Investigating Performance Degradation
The agent is running but slowly. Instead of restarting, the engineer uses get_trace to capture the slow run. They then analyze the metrics with get_trace_metrics to see if the slowdown is due to an unexpected spike in token usage or an inefficient tool call.
The Tradeoffs
Treating everything as logs
Reading raw logs and trying to piece together the agent's path using regex. This is slow, error-prone, and misses context like token counts or timing data.
→
Don't just read logs. Use get_trace to get the full event sequence, and then use get_span to isolate the exact time boundaries and inputs for the specific action you need to debug.
Ignoring cost metrics
Deploying an agent without monitoring costs. You get a successful run, but the bill is unexpectedly high because of a few expensive, unnecessary tool calls.
→
Before deployment, run test cases and immediately use get_trace_metrics. This tells you the exact token count and associated cost for the workflow, making budget planning accurate.
Relying only on project name
Assuming the current agent run belongs to the right project just because the name looks right. The project ID might change, leading to incorrect telemetry data.
→
Always start by calling get_project to confirm the current, active project ID. This locks down your scope and ensures all subsequent get_trace and get_span calls are correctly scoped.
When It Fits, When It Doesn't
Use this server if you need deep, actionable visibility into how and why an AI agent executed a task, especially when performance or cost matters. If your goal is simply to see the final output, you don't need this. You need to know the mechanics. If you only need to see a list of available tools or a simple data lookup, a standard database connector is fine. But if the complexity is in the process—the multi-step decision-making, the tool usage, the token count—then this is the right tool. Use get_project to define the scope, get_trace for the sequence, get_span for the details, and get_trace_metrics for the numbers. Don't skip steps; they build on each other.
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 server provides 4 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Debugging an agent's failure shouldn't mean clicking through four different dashboards.
Right now, when an agent fails, you're stuck. You check the main logs for the error, then copy the ID over to the billing dashboard to check costs, then switch to the execution history to see what happened, and maybe finally jump to a separate monitoring tool to check latency. It's a dozen clicks and three different systems just to get one answer.
With AgentOps, you connect your agent to the MCP server. You run the tool, and you get the full picture. You see the entire execution flow, the decision points, and the associated cost, all in one place. You get the diagnosis, not just the symptoms.
AgentOps (Agent Telemetry and Monitoring) MCP Server: Get the full picture.
Forget manually correlating timestamps and IDs across different services. Instead, you run a single call that pulls the full trace data. You get the raw event sequence, the exact inputs used for every tool call, and the resulting metrics, all without leaving your client.
This changes everything. You stop reacting to errors and start predicting them. You know exactly where the system broke and why.
Common Questions About AgentOps MCP
How do I use the `get_project` tool to start monitoring? +
get_project pulls your current project details. It's the first step you should run to confirm the scope and ID for all subsequent monitoring calls. This confirms you're looking at the right agent deployment.
What is the difference between `get_trace` and `get_span`? +
get_trace gives you the entire story—the sequence of events from start to finish. get_span lets you zoom in on one specific moment, like a single tool call or an LLM response, for deep inspection.
How do I use `get_trace_metrics` to check costs? +
You provide a trace ID to get_trace_metrics. It returns quantitative data, including the total token count and the estimated dollar cost for that specific agent run.
Does AgentOps track multi-agent communication? +
Yes. The telemetry tracks interactions across the entire agent graph, letting you see how different agents passed data and influenced the final outcome.
What data does `get_trace` return? +
get_trace returns a structured view of the agent's execution path, including timestamps, the type of event, and the related tool calls.
How does `get_span` help me debug a slow agent workflow? +
It shows the exact timing of an operation. get_span provides start and end timestamps, letting you pinpoint which tool call or LLM interaction slowed down the agent.
Can I use `get_trace_metrics` to track token costs for a specific period? +
Yes, you pass the trace ID and the time window. get_trace_metrics returns granular data on total token count (prompt vs. completion) and the associated dollar cost for that trace.
What is required to use `get_project` for the first time? +
You need your AgentOps API Key. get_project uses this key to identify and retrieve the high-level details for your active monitoring project.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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