Compatible with every major AI agent and IDE
What is the Accept Language Parser MCP Server?
When a global routing agent reads Accept-Language: en-US,pt-BR;q=0.9,fr;q=0.8, it needs to correctly parse quality weights and determine the user's preferred language. This MCP does it deterministically.
The Superpowers
- RFC 7231 Compliant: Parses quality values (q-factors) exactly as specified by the HTTP standard.
- Priority Ordered: Returns languages sorted by quality weight, with the preferred language first.
Built-in capabilities (1)
Pass the raw header value (e.g. "en-US,pt-BR;q=0.9,fr;q=0.8") and receive a priority-ordered list of languages with their quality weights. Never try to parse quality weights manually. Parses HTTP Accept-Language headers into an ordered list of user language preferences with quality weights. Essential for global routing and i18n agents
Why VS Code Copilot?
GitHub Copilot Agent mode brings Accept Language Parser data directly into your VS Code workflow. With a project-scoped config, the entire team shares access to 1 tools. Copilot queries live data, generates typed code, and writes tests from actual API responses, all without leaving the editor.
- —
VS Code is used by over 70% of developers. adding MCP tools to Copilot means your team can leverage external data without leaving their primary editor
- —
Project-scoped MCP configs (
.vscode/mcp.json) let you commit server configurations to your repository, ensuring the entire team shares the same tool access - —
Copilot's Agent mode integrates MCP tools seamlessly with file editing, terminal commands, and workspace search in a single agentic loop
- —
GitHub's enterprise compliance and audit features extend to MCP tool usage, providing visibility into how AI interacts with external services
Accept Language Parser in VS Code Copilot
Accept Language Parser and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Accept Language Parser to VS Code Copilot through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Accept Language Parser in VS Code Copilot
The Accept Language Parser MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 1 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in VS Code Copilot only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
Accept Language Parser for VS Code Copilot
Every tool call from VS Code Copilot to the Accept Language Parser MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
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.
Which VS Code version supports MCP?
MCP support requires VS Code 1.99 or later with the GitHub Copilot extension. Ensure both are updated to the latest version. Older versions of Copilot may not expose the Agent mode toggle.
How do I switch to Agent mode?
Open the Copilot Chat panel and look for two mode options: "Ask" and "Agent". Click "Agent" to enable autonomous tool calling. In Ask mode, Copilot provides conversational answers but cannot invoke MCP tools.
Can I restrict which MCP tools Copilot can access?
Yes. VS Code shows a tool consent dialog before any MCP tool is invoked for the first time. You can also configure tool access policies at the organization level through GitHub Copilot settings.
Does MCP work in VS Code Remote or Codespaces?
Yes. MCP servers configured via .vscode/mcp.json work in Remote SSH, WSL, and GitHub Codespaces environments. The MCP connection is established from the remote host, so ensure the server URL is accessible from that environment.
MCP tools not available
Ensure you are in Agent mode in Copilot Chat. MCP tools only appear in Agent mode.
Explore More MCP Servers
View all →
Teyuto
10 toolsBuild your own video streaming platform with monetization, audience analytics, and content management for video creators.

Daily.co
50 toolsManage video calls and WebRTC infrastructure via Daily.co — create rooms, track participants, and control meeting sessions directly from your AI.

FireHydrant
12 toolsManage incidents, services, and responder teams via AI agents with FireHydrant.

ParseHub
10 toolsControl advanced cloud scraping projects via ParseHub — list targets, dispatch headless runs, trace crawler status, and fetch extracted datasets directly via AI.
