Treblle MCP. Log Every Request and Response in One Place
Treblle connects your AI agent to an API observability platform. It lets you send full API request and response payloads directly into a central dashboard for real-time monitoring. You track performance, debug errors, and document endpoints without manual setup. Stop losing context when debugging; get instant visibility into every API call.
Give Claude and any AI agent real-world access
Send the entire raw body of an API interaction—both the request sent out and the response received back—to Treblle.
Treblle automatically scrubs private information, like passwords or SSNs, from the data before logging it, keeping your system secure.
Add specific identifiers, such as user IDs or trace IDs, to log entries so you can filter and group related calls later.
Observe API health and traffic patterns in real time as your agent runs tests or processes live data.
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What AI agents can do with Treblle with 1 Tool
These tools allow you to send complete API request and response data directly into Treblle for instant observability.
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 Treblle MCPIngest Api Data
Sends a complete set of API request and response data to Treblle, automatically masking any sensitive fields like passwords or credit card...
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 each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Treblle, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Treblle. 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 CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
The Pain of Fragmented API Logging
Today, if an API call fails, you end up in a cycle of clicking. You jump from the terminal output to a separate log management system, then maybe to Jira to check ticket details. You're copying headers here, pasting payloads there, and cross-referencing timestamps across three different dashboards just to understand a single failure.
With this MCP, your agent handles all that mess. It captures the entire lifecycle of an API interaction—the request, the response, and any errors in between—and deposits it into Treblle instantly. You get one place where everything you need actually lives.
Treblle MCP: Full Visibility with `ingest_api_data`
You stop manually formatting payloads and running complex, multi-step scripts just to get a clean log. The agent handles the data structure, making sure both success and failure states are captured correctly.
The result is immediate observability. You don't have to guess what happened; you see it all logged, masked, and ready for documentation right away.
What Treblle MCP does for your AI
When your code talks to external services, that conversation generates massive amounts of data—logs, headers, payloads. Sending all that raw traffic manually is a nightmare. This MCP changes that. You connect your agent and it lets you push the entire lifecycle of an API interaction directly into Treblle. Your agent handles sending both the original request and the full response body automatically.
You don't just log; you get observability. As data streams in, Treblle tracks performance metrics and spots errors instantly. Plus, security is built-in: it masks sensitive fields like credit card numbers before anything gets saved. This means your team can focus on debugging and documentation, not worrying about compliance or lost context.
When you connect this through Vinkius, you're giving your AI client a single point of truth for all your API traffic.
This is crucial for developers needing a reliable record of what worked, what failed, and how fast it was.
019e38ff-04cf-7372-9c62-eff901ece6a8 How to set up Treblle MCP
The bottom line is that your agent handles all the messy logging details; you just get clean, actionable observability data.
First, subscribe to this MCP and provide Treblle with your required API Key and SDK Token.
Next, tell your AI client to send the full payload (request body + response body) using the ingest_api_data tool.
Finally, the data appears in your Treblle dashboard, where you can view performance metrics, check for errors, and access masked logs.
Who uses Treblle MCP
This MCP solves the problem of manual API logging. It's for the Backend Developer who gets frustrated having to jump between multiple dashboards just to debug a single failed endpoint. Or the DevOps Engineer tired of relying on limited terminal logs when tracing complex, multi-service failures.
Uses this MCP to quickly log and test API interactions, building comprehensive documentation automatically as they write code.
Monitors API health across different environments from the terminal, tracing traffic patterns without needing manual instrumentation scripts.
Captures full context of API errors during testing phases, ensuring every failure is logged with enough detail to reproduce it later.
Benefits of connecting Treblle MCP
Stop guessing why an API failed. By using the ingest_api_data tool, you get full request and response payloads, allowing your agent to show you exactly where the breakdown happened.
Security is handled automatically. Treblle masks sensitive fields like SSNs and credit card numbers before logging anything. You gain observability without sacrificing compliance.
Build better documentation faster. Every time you log a successful call using this MCP, you are building out a detailed record of that endpoint's expected behavior.
Improve debugging cycles dramatically. Instead of relying on fragmented terminal outputs, you can trace complex error flows and performance metrics right inside Treblle.
Context is king. You can attach custom metadata like user IDs or environment names to every log entry, making it simple to filter thousands of calls down to the exact interaction you need.
Treblle MCP use cases
Debugging a Payment Failure
A QA Engineer runs a payment test and gets a generic '500 Internal Server Error.' Instead of manually copying headers, they ask their agent to run the transaction and log it with ingest_api_data. Treblle captures the full response payload, revealing that the failure was due to an outdated card token, not a server issue.
Auditing API Changes
A Backend Developer needs to prove what data was exchanged between services last week. They use their agent to run targeted read operations and log them via ingest_api_data. Treblle provides a historical, auditable record of every successful call.
Troubleshooting Slow Endpoints
A DevOps team notices the /user/details endpoint is running slowly. They run diagnostic calls and log them to Treblle. By viewing the real-time performance metrics in the dashboard, they pinpoint that a specific database query within the response body is causing the bottleneck.
Building API Documentation
A new team member needs to understand an undocumented endpoint. They run several test calls and use ingest_api_data to log them. Treblle processes this stream of data, creating a clean, documented view of the request/response structure for future developers.
Treblle MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Logging only success status codes
A developer logs calls but only includes the HTTP status (e.g., '200 OK'). When an error occurs, they have no idea why it failed.
Use ingest_api_data to send the full request and response payloads. This captures everything, including detailed error messages within the body itself.
Ignoring sensitive data masking
Logging all raw traffic, including passwords or credit card numbers, creates a massive security risk that could lead to compliance failure.
This MCP handles masking automatically. The ingest_api_data tool ensures private fields are scrubbed before the data ever hits your dashboard.
Copy-pasting logs into spreadsheets
A QA engineer manually copies error messages and payloads from a terminal into Excel to share with the team. This process is slow, error-prone, and never captures context.
Let your agent use this MCP to ingest API data directly. Everything is centralized in Treblle's dashboard for instant searching and viewing.
When to use Treblle MCP
Use this MCP if you need comprehensive observability into the structure and content of your APIs—specifically, if you need to track full request/response bodies or debug performance failures. It’s built for structured data logging, making it ideal for QA and backend teams.
Don't use this if all you need is a simple chat log of what the user typed into the terminal, or if you only care about high-level operational flags (e.g., 'User logged in'). For those cases, a basic message logging MCP will work better. This tool requires sending structured payloads to get value; it's not for general conversation snippets.
Frequently asked questions about Treblle MCP
How do I use Treblle MCP to document my existing APIs? +
You repeatedly run test calls through your agent using the ingest_api_data tool. Treblle ingests these successful interactions, automatically building out a comprehensive record of the endpoint's inputs and outputs for documentation.
Does Treblle MCP mask sensitive data? +
Yes, it does. The ingest_api_data tool includes built-in security features that automatically detect and mask private fields like credit card numbers or passwords before they are stored.
Is Treblle MCP only for debugging errors? +
No, it's for full observability. While excellent for error tracking, you should also use it to log successful calls to monitor performance and build documentation.
What data does `ingest_api_data` require? +
It requires the full request payload (what was sent) and the full response payload (what came back). Sending both gives you complete context for analysis in Treblle.
Can I add custom labels to my API logs using Treblle MCP? +
Yes. You can attach specific metadata, like unique user IDs or trace numbers, to every log entry. This allows you to filter huge amounts of data down to a very specific set of calls.