# AgentOps MCP for AI Agents AI Agent Connect

> AgentOps MCP helps you monitor and observe your AI agents in real time. It tracks execution traces, individual spans, and project-level metrics so you can see exactly how your agentic workflows perform, where they bottleneck, and how much they cost to run.

## Overview
- **Category:** developer-tools
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_OCln8CM8I7G7rNCo6QKijZPwPeMGY7W7gj6qBqEG/ai-agent-connect
- **Tags:** telemetry, observability, ai-agents, tracing, monitoring

## Description

This AgentOps MCP connects your agentic workflows to AgentOps, giving you a clear view of every step your agent takes. When you're building agents, things get messy fast. You need to know why a specific loop failed or why a certain tool call took ten seconds. Instead of guessing what's happening behind the scenes, you get a live look at the telemetry. You can see the decision-making flow, identify where the logic breaks, and keep a close eye on your token spend. It's about moving from "I think it works" to "I know exactly how it's working." You can find this and thousands of other tools in the Vinkius catalog to keep your stack organized. It's the difference between flying blind and having a full dashboard for your production agents. This means you can spot a failing tool call in a multi-step sequence immediately, rather than waiting for the whole process to timeout. It gives you the data needed to justify your infrastructure spend to stakeholders and ensures your agents are performing as expected in the wild. By integrating this into your workflow, you get a reliable way to audit every interaction, ensuring your agents remain predictable and efficient as you scale.

## Tools

### get_trace_metrics
Pull the specific token counts and dollar costs associated with a particular agentic trace. This provides clear data on your operational spend.

### get_trace
Look up the full execution history of a specific agent run to see how it navigated a task. This helps you visualize the step by step logic flow.

### get_project
Fetch the high-level details and status of your current AgentOps project. Use this to manage multiple environments and view project health.

### get_span
Drill down into a single step, like a web search or a database write, to see the exact input and output. It isolates specific errors in tool calls.

## Prompt Examples

**Prompt:** 
```
What happened during the last run of my production agent?
```

**Response:** 
```
### Last Run Summary
**Trace ID:** `tr_992834`  
**Status:** Success  
**Duration:** 14.2s  
**Steps Taken:**
1. **Web Search:** Queried "market trends"
2. **Data Extraction:** Parsed 3 results
3. **Summary Generation:** Created final report

Everything completed successfully in 3 steps.
```

**Prompt:** 
```
Show me the token costs for the last 5 traces.
```

**Response:** 
```
| Trace ID | Tokens (P/C) | Est. Cost |
|---|---|---|
| tr_992834 | 1,450 (800/650) | $0.028 |
| tr_992833 | 2,100 (1200/900) | $0.042 |
| tr_992832 | 800 (400/400) | $0.015 |
| tr_992831 | 3,200 (2000/1200) | $0.078 |
| tr_992830 | 1,100 (600/500) | $0.022 |

**Total Cost:** $0.185
```

**Prompt:** 
```
Why did the web search tool fail in the last run?
```

**Response:** 
```
The failure occurred in **Span `sp_4452`** (Web Search Tool Call).

*   **Input:** `query: agent observability`
*   **Error:** `TimeoutException: Request took longer than 10 seconds`
*   **Context:** The agent attempted to reach the search endpoint three times before the trace recorded a failure.
```

## Capabilities

### Track execution traces
See the full path of an agent's logic to understand how it navigated a task.

### Check project-level details
Manage your agent environments and view high-level project status.

### Inspect individual spans
Drill down into specific tool interactions to debug exact inputs and outputs.

### View token usage and costs
Get detailed metrics for every trace to monitor your real-time spend.

### Identify performance bottlenecks
Spot where complex agentic loops are slowing down or failing.

## Use Cases

### Debugging a failing loop
An AI Engineer notices an agent is stuck in a loop. They ask the agent to find the trace, and use get_trace to see where the logic started looping.

### Budget auditing
A Product Manager wants to know the cost of a new feature. They ask the agent to pull metrics for the last 100 runs using get_trace_metrics.

### Tool call inspection
A developer needs to see what parameters were sent to a search tool. They use get_span to see the exact query and the results returned.

### Project overview
A team lead wants to verify their production environment is active. They use get_project to check the status and ID of their current project.

## Benefits

- Stop guessing about agent behavior by using get_trace to see every decision point in a sequence.
- Keep your budget in check by using get_trace_metrics to see exactly how many tokens each task consumes.
- Debug tool interactions faster by using get_span to isolate which specific API call is failing.
- Manage multiple environments easily by using get_project to see your high-level project stats.
- Reduce time spent on troubleshooting by identifying bottlenecks in agentic loops before they hit production.
- Improve ROI by correlating agent performance with actual operational costs in real time.

## How It Works

The bottom line is you get instant visibility into your agent's internal logic and costs without leaving your workspace.

1. Subscribe to the AgentOps MCP in the Vinkius marketplace.
2. Add your AgentOps API Key to your configuration.
3. Query your agent's performance directly from your AI client.

## Frequently Asked Questions

**What is AgentOps MCP for?**
It's a tool that lets you see the "inner thoughts" and performance data of your AI agents. You get a clear view of how they make decisions and how much they cost to run.

**How does AgentOps MCP help with costs?**
It connects to your telemetry to pull specific token usage and dollar amounts for every agent run. This helps you spot expensive loops and manage your budget.

**Can I use AgentOps MCP to debug my agent's logic?**
Yes, it lets you pull up specific execution traces to see the exact path an agent took. You can see every tool call and step it performed.

**How do I see specific tool interactions?**
You can use the Connector to look at individual spans. This shows you the exact input and output for every single action your agent takes.

**Is AgentOps MCP for production agents?**
It's ideal for production because it provides the structured telemetry needed for reliable monitoring. You can track performance across your entire project.

**Does AgentOps MCP show me my project details?**
Yes, it can retrieve high-level information about your AgentOps projects, like their status and current activity.

**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.