User-Agent Parser MCP. Stop AI agents from hallucinating client data from log files.

You gotta deal with server logs or firewall reports, right? You see those User-Agent strings—they look like a mess. They’re just raw text: Mozilla/5.0 (iPhone; CPU iPhone OS 16_5 like Mac OS X) AppleWebKit/605.1.15. If your agent reads that string without help, it'll guess, and those guesses are usually wrong.

The User-Agent Parser fixes that mess. It’s built around the parse_ua tool. This function takes any raw User-Agent string you feed it and spits out clean, structured JSON. It stops your AI client from having to play detective with gibberish headers.

When you call parse_ua, you pass in that messy string. The server then surgically decodes the data. You get back an exact JSON object detailing three key things: the client's browser, the operating system (OS), and the device platform. It prevents your agent from making assumptions about specs when it reads logs.

Think about what you do with that clean data. Instead of vague guesses, you're working with verified facts. The JSON output cleanly separates the browser version, the precise OS name, and the physical device type. This makes debugging platform-specific bugs accurate because your agent isn’t relying on guesswork; it’s using structured reality.

The parser handles all that messy HTTP header data. It recognizes patterns usually hidden in plain sight—the differences between desktop clients, mobile apps, and specific operating system builds. You pass the raw string, and you get back machine-readable clarity.

It’s all about precision here. We're talking about reliable extraction of client specifications directly from logs. The tool doesn't just read the string; it interprets it using industry standards to identify what's actually running on the user's end. It separates the browser engine data from the underlying OS kernel data, giving you a complete picture.

When your agent needs to know if a specific feature works only on iOS 16 or Mac OS X, this tool provides that certainty. You don't get 'iOS-like'; you get the actual version number. You don't get 'mobile device'; you get the platform and the model context.

The process is simple: Pass any raw User-Agent string to parse_ua. It returns a standardized JSON structure. This means every piece of data—the browser name, its specific version, the OS family it runs on, and the underlying hardware type—is in labeled fields your agent can use immediately.

If you’re building an application that analyzes user traffic or diagnoses client-side issues, this is non-negotiable. The messy logs used to cripple analysis by forcing developers to write complex regex just to extract a simple version number. Now, your AI client handles the parsing overhead for you.

It takes raw data—the ugly strings found in Referer or User-Agent headers—and transforms it into actionable JSON records. You get the browser name (Chrome, Safari, Edge), the OS detected (Windows NT 10.0, iPhone OS), and the device platform type (Mobile, Desktop). Each data point is isolated and perfectly formatted.

The mechanism ensures that even complex strings involving multiple operating systems or emulated environments are broken down correctly. It knows how to differentiate between an 'Engine' reported in the header versus the actual 'Browser Name'. This level of granularity saves you time when troubleshooting user-reported errors across diverse client bases.

You get clean data points, ready for direct use by your agent.

It’s pure, structured metadata extraction. No interpretation needed from your end; just feed it the log line, and let parse_ua do its job of returning a comprehensive JSON object that details every relevant piece of client information.