Vinkius
Highlight

Highlight MCP for AI. Monitor user flow and debug performance metrics.

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

Highlight (Session Replay & UX) MCP on Cursor AI Code EditorHighlight (Session Replay & UX) MCP on Claude Desktop AppHighlight (Session Replay & UX) MCP on OpenAI Agents SDKHighlight (Session Replay & UX) MCP on Visual Studio CodeHighlight (Session Replay & UX) MCP on GitHub Copilot AI AgentHighlight (Session Replay & UX) MCP on Google Gemini AIHighlight (Session Replay & UX) MCP on Lovable AI DevelopmentHighlight (Session Replay & UX) MCP on Mistral AI AgentsHighlight (Session Replay & UX) MCP on Amazon AWS Bedrock

How this MCP server connects to your AI agent

Highlight MCP sends raw text logs, structured OTLP JSON logs, and detailed traces directly into your Highlight dashboard. Use this MCP to centralize all observability data—from backend service activity to user interaction paths—for deep performance monitoring.

What AI agents can do with Highlight (Session Replay & UX) Automation

Ingest otlp logs

This sends structured OTLP JSON logs into Highlight for deep context and metadata tracking.

Ingest otlp traces

Use this to send full OTLP JSON traces, allowing you to visualize complete request paths.

Ingest raw log

This sends simple, unformatted text log messages directly into Highlight.

Send raw text log messages

You can send simple, unstructured text logs from your backend services to Highlight.

Ingest structured OTLP logs

The MCP accepts and processes complex, structured logs formatted in the OpenTelemetry Protocol (OTLP) JSON standard.

Track request performance traces

You can send full OTLP traces to visualize entire request flows and pinpoint exactly where latency occurs.

Correlate logs with user sessions

The data lands in Highlight, allowing you to tie backend failures back to specific monitored user experiences.

Included with Plan

Waiting for input…

AI Agent

What AI agents can do with Highlight (Session Replay & UX) with 3 Tools

These tools allow you to send raw text messages, structured OTLP logs, and detailed traces directly to Highlight for monitoring.

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 Highlight (Session Replay & UX) on Vinkius

Ingest Otlp Logs

This sends structured OTLP JSON logs into Highlight for deep context and metadata tracking.

Ingest Otlp Traces

Use this to send full OTLP JSON traces, allowing you to visualize complete request...

Ingest Raw Log

This sends simple, unformatted text log messages directly into Highlight.

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 Highlight 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 Highlight (Session Replay & UX), 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
Highlight 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 Highlight. 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.

Built on the Model Context Protocol (MCP) for 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 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Finding the root cause of an outage used to mean jumping through hoops.

Today, when something breaks, you find one log snippet in the terminal, then copy a trace ID into your dashboard, and finally switch tabs to look at user behavior. It’s manual, it's slow, and you always feel like you missed some critical piece of context that only lives somewhere else.

With this MCP, everything lands together. Your agent routes raw logs, structured JSON data, and performance traces into one place in Highlight. You stop searching across tabs; you just start reading the single source of truth.

Ingesting all signals with Highlight's tools.

You no longer have to manually write scripts or build custom pipelines every time a new data source comes online. You just tell your AI client to use the MCP and send the data, letting it handle which tool—`ingest_otlp_logs`, `ingest_otlp_traces`, or `ingest_raw_log`—is needed.

The difference is that you get total signal coverage. You don't just see *that* something broke; you see the full, multi-layered story of how and why it broke.

What your AI can actually do with this

Need to figure out why a feature is slow or why an error popped up? This MCP lets you feed complex system signals—raw text, structured JSON logs, and full request traces—right into your Highlight project. You don't have to jump between five different dashboards just to correlate a user session with the backend failure.

Instead, your agent manages all that data ingestion for you.

The process is straightforward: send the data using natural conversation, and it lands in Highlight. Whether you’re debugging a specific API call or trying to understand general system health, this MCP gives your AI client access to every signal type needed. When you find observability tools on Vinkius, this one handles the messiest part—getting all the varied logs into a single source of truth for root cause analysis.

Built · Hosted · Managed by Vinkius Highlight MCP - Monitor Session Replay & UX
Server ID 019e5d23-7122-703d-a3e4-a7ddb532a4e0
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Questions you might have

How does ingest_raw_log differ from ingest_otlp_logs in Highlight? +

It's about structure. ingest_raw_log handles plain text, which is great for debugging stack traces or simple messages. However, ingest_otlp_logs forces a structured JSON format, making the data searchable and filterable by resource type.

Do I need all three tools to monitor performance? +

Yeah, you should. While OTLP traces (ingest_otlp_traces) show the path of a request, raw logs are often where the specific failure message lives, and structured logs (ingest_otlp_logs) provide the metadata needed to find that failure across thousands of records.

Can I use this MCP for non-API logging? +

Yep. If you're capturing user behavior or console messages that don't come from a standard API endpoint, you can still funnel them using ingest_raw_log to keep the context visible in Highlight.

Is ingest_otlp_traces only for microservices? +

Nah. While it's built for complex service calls, you can use it anytime you want to map a sequence of actions—even within a single application process—to track performance.

What specific parameters do I need to provide when using ingest_otlp_logs? +

You must include the highlight.project_id in the payload. This attribute tells your agent exactly which project within Highlight should receive the structured OTLP logs.

If I use ingest_raw_log, how do I handle sensitive user data? +

You are responsible for sanitizing any PII before calling this tool. While you can send raw text, always scrub names, emails, or IDs first to maintain privacy and security.

If I run ingest_otlp_traces many times in a row, is there a rate limit? +

The system handles large data volumes, but rapid-fire calls may hit API limits. It's best practice to batch your trace payloads or implement a small delay between ingestion runs.

What happens if the JSON I pass to ingest_otlp_logs is malformed? +

The agent will return an error message detailing the schema failure. It won't process incomplete data, requiring you to fix your payload before making a successful call.

How can I send a basic text log message to my dashboard? +

You can use the ingest_raw_log tool. Simply provide the service name and the message content, and it will be sent directly to Highlight.

Does this server support structured OpenTelemetry logs? +

Yes! Use the ingest_otlp_logs tool to send structured logs in OTLP JSON format. Ensure your payload includes the project ID attribute.

Can I visualize request traces using this integration? +

Absolutely. The ingest_otlp_traces tool allows you to send OTLP JSON traces to Highlight, helping you track request spans and performance.

Built & Managed by Vinkius 30s setup 3 tools

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

No hosting. No infrastructure. No complex setup.
All 3 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

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.

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