Accept Language Parser MCP for AI. Get the definitive order of preferred languages for routing.
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Parse Accept Language By taking a raw HTTP header string and returning an ordered, deterministic list of language preferences with their quality weights.
This correctly follows RFC 7231 standards, telling your agent exactly which languages a user prefers for global routing or content negotiation.
What your AI can do
Parse accept language
Passes a raw Accept-Language header string and returns an ordered list of user language preferences with their quality weights.
Reads a raw header string and outputs the user's preferred languages in order of importance.
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Accept Language Parser (1 Tools)
The single tool allows your agent to parse raw HTTP Accept-Language header strings into a prioritized, ordered list of user language preferences.
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Start using Accept Language Parser on VinkiusParse Accept Language
Passes a raw Accept-Language header string and returns an ordered list of user language preferences with their quality weights.
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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.
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Trying to guess a user's language preference from raw headers is a nightmare.
Right now, when your agent reads an 'Accept-Language' header, it sees a mess: `de,en-GB;q=0.7,ja;q=0.3`. The code has to manually parse the commas, figure out which codes have quality weights, and then sort them all based on those floating point numbers. If your logic fails to respect even one of those tiny q-factors, you risk sending content in the wrong language or order.
With this MCP, that entire headache vanishes. You simply feed the raw header string into the tool. It outputs a perfectly ordered list: the languages sorted from most preferred to least preferred. Your agent gets clean, actionable data without having to worry about compliance details.
Getting Language Priority with parse_accept_language
Manually parsing this header means writing complex regex and handling floating point comparisons across multiple languages. You're constantly fighting edge cases where the weights are close, or where a language is listed without a weight, causing unpredictable behavior.
Now you can call `parse_accept_language`. It handles all the complexity behind the scenes. Your agent gets a deterministic, sorted list, making global routing simple and reliable.
What your AI can actually do with this
When you build an application that serves users globally, figuring out the right language is non-negotiable. You can't just guess based on IP location. This MCP reads the raw 'Accept-Language' header—that string of codes and quality weights sent by a browser—and turns it into a clean list. It processes complex inputs like "en-US,pt-BR;q=0.9,fr;q=0.8" correctly, respecting every quality score (or q-factor) exactly as the HTTP standard dictates.
The result is a prioritized sequence of languages, with the user's top choice listed first. This eliminates guesswork from your routing logic. If you're using Vinkius for other services, this MCP ensures language detection is always rock solid and compliant.
019e385f-dffc-7044-8e78-ad98c51cde53 Here's how it actually works
The bottom line is you get a reliable sequence of language preferences that your code can use immediately for routing or content selection.
You pass the full 'Accept-Language' HTTP header value to your agent.
The MCP processes that string, accurately calculating and sorting all quality weights (q-factors) against the RFC 7231 standard.
Your agent receives a clear list: the languages sorted from most preferred to least preferred.
Who is this actually for?
Anyone building anything for an international audience needs this. Think platform architects, backend engineers, and devops staff who manage global microservices. If you deal with APIs that need to respond in the user's native tongue, this is what you use.
Integrates language detection into API gateways, ensuring requests are routed correctly based on quality weights.
Configures global load balancers or traffic routing rules that depend on accurate client-side language negotiation.
Writes the internationalization (i18n) logic layer, using this MCP to determine content delivery priority before rendering pages.
What Changes When You Connect
Stops guesswork in global routing. Instead of making assumptions, your agent gets a guaranteed priority list based on strict HTTP standards.
Handles complex inputs correctly. It accurately parses quality weights (q-factors) like pt-BR;q=0.9, which simpler parsers ignore or misinterpret.
Saves time building i18n logic. You don't need to write custom RFC 7231 compliance code; this MCP handles the heavy lifting for you.
Improves user experience across regions. By knowing the true preference, you serve content in the language the user actually asked for.
Works with any agent environment. Connect your logic via Vinkius to use this reliable data source from Claude, Cursor, or Windsurf.
See it in action
A global e-commerce site needs to load the checkout page.
The client sends a header listing English and Spanish, but with different priorities. Instead of defaulting to US English, your agent uses this MCP to see that the user actually prefers French (q=0.8) over Spanish (q=0.7), ensuring the correct localized content loads for checkout.
A multi-region API gateway handles requests.
The API needs to decide which version of its documentation endpoint to expose. By passing the header through this MCP, the agent gets a clear list that prioritizes Japanese (q=1) over Chinese (q=0.9), routing the request immediately to the correct backend service.
A content management system is serving localized articles.
The CMS receives requests from various clients. Using this MCP, the agent determines that while German and English are listed, the user's true preference is Italian (q=0.85). The system then pulls and serves the article in high-quality Italian.
Building a complex microservice chain.
A series of services must process incoming data according to language rules. This MCP provides the definitive, ordered list upfront, allowing subsequent services (like validation or database calls) to operate only on guaranteed preferences.
The honest tradeoffs
Manual header splitting
Trying to use basic string split functions in your code. You might get 'en-US, pt-BR;q=0.9' and incorrectly assume the order or miss the quality weights entirely.
Always pass the raw value to parse_accept_language. This MCP handles the complexity of parsing the header string and reliably extracts both the language code and its precise quality weight.
Ignoring RFC compliance
Assuming that if two languages are listed, they must be equally weighted. For example, treating 'fr' (no q-factor) as having the same weight as 'en-US;q=1'.
Use this MCP to calculate weights deterministically. It enforces RFC 7231 rules, ensuring that a missing quality factor is correctly treated relative to weighted factors.
Hardcoding language fallbacks
Building logic that simply checks for 'en' then defaults to 'es'. This breaks if the user sends headers indicating they prefer Spanish over English, but with a higher weight.
Let this MCP determine the order. It gives you the true priority list, letting your agent make decisions based on data, not assumptions.
When It Fits, When It Doesn't
Use this MCP if your application's routing or content selection depends directly on an HTTP 'Accept-Language' header value and needs to be RFC 7231 compliant. This is essential for any global service. Don't use it if you only need to check for the presence of a language (a simple 'does this list contain French?'). For that, basic string search works fine. However, if you need to know which languages are preferred and in what sequence, or if those preferences rely on complex quality weights like q=0.9, then use parse_accept_language. Never assume the order; let this MCP tell you.
Questions you might have
How does parse_accept_language handle multiple quality weights? +
It processes them accurately according to RFC 7231. It correctly sorts the languages based on their assigned 'q' factor, making sure the highest weighted language is listed first.
Does parse_accept_language support only US English headers? +
No. This MCP supports all recognized HTTP Accept-Language formats globally and reliably parses weights for any combination of locale codes.
Is this better than just looking at the first language listed? +
Yes, absolutely. The first listed language might not be the most preferred; it only means it's listed first. This MCP determines true preference based on calculated quality weights.
Can I use parse_accept_language with my existing i18n logic? +
You can. By feeding this MCP the raw header, you get a guaranteed priority list that should slot right into your current decision-making process for content delivery.
What happens if I pass malformed data to parse_accept_language? +
It handles invalid headers gracefully. The tool won't crash your process; instead, it returns a clear error or the valid subset of languages found. This built-in validation keeps your global routing agent stable even when receiving bad HTTP data.
Is parse_accept_language performant enough for high-volume API calls? +
Yes, it's highly optimized for speed and low latency. Because it's a dedicated parsing function, you won't encounter bottlenecks common with general string processing. You can expect near-instantaneous results even when running through hundreds of headers per second.
How do I integrate parse_accept_language into my existing backend service? +
You simply call it as a function, passing the raw Accept-Language header string. It's built to drop right into standard environments like Python or Node without requiring complex setup steps or custom middleware.
What result does parse_accept_language return if the header is completely missing? +
The function handles null, empty, or absent inputs by returning a predictable, empty ordered list. This reliable behavior allows your agent to execute a safe fallback logic without ever encountering a runtime error.
What is a quality weight (q-factor)? +
A value from 0 to 1 indicating preference. q=1 (default) is highest priority. q=0 means the language is explicitly not accepted.
Does it handle regional subtags? +
Yes. pt-BR is parsed as code=pt, region=BR. en-US as code=en, region=US. The region is separated from the language code automatically.
What if no quality value is specified? +
Languages without an explicit q-value default to q=1 (highest priority), following the HTTP specification.
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