Accept Language Parser MCP Server for LlamaIndexGive LlamaIndex instant access to 1 tools to Parse Accept Language

Name: Accept Language Parser
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Accept Language Parser as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Accept Language Parser MCP Server for LlamaIndex is a standout in the Productivity category — giving your AI agent 1 tools to work with, ready to go from day one.

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Accept Language Parser. "
            "You have 1 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Accept Language Parser?"
    )
    print(response)

asyncio.run(main())

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About 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.

LlamaIndex agents combine Accept Language Parser tool responses with indexed documents for comprehensive, grounded answers. Connect 1 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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.

The Accept Language Parser MCP Server exposes 1 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 1 Accept Language Parser tools available for LlamaIndex

When LlamaIndex connects to Accept Language Parser through Vinkius, your AI agent gets direct access to every tool listed below — spanning http-headers, localization, language-detection, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

parse

Parse accept language on Accept Language Parser

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

Connect Accept Language Parser to LlamaIndex via MCP

Follow these steps to wire Accept Language Parser into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 1 tools from Accept Language Parser

Why Use LlamaIndex with the Accept Language Parser MCP Server

LlamaIndex provides unique advantages when paired with Accept Language Parser through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Accept Language Parser tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Accept Language Parser tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Accept Language Parser, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Accept Language Parser tools were called, what data was returned, and how it influenced the final answer

Accept Language Parser + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Accept Language Parser MCP Server delivers measurable value.

Hybrid search: combine Accept Language Parser real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Accept Language Parser to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Accept Language Parser for fresh data

Analytical workflows: chain Accept Language Parser queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Accept Language Parser in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Accept Language Parser immediately.

"Parse this Accept-Language header: en-US,pt-BR;q=0.9,fr;q=0.8"

"What is the user's preferred language from: de,en-GB;q=0.7,ja;q=0.3"

"How many languages does the browser support based on this header: zh-CN,zh;q=0.9,en;q=0.8,ko;q=0.7,ar;q=0.6"

Troubleshooting Accept Language Parser MCP Server with LlamaIndex

Common issues when connecting Accept Language Parser to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Accept Language Parser + LlamaIndex FAQ

Common questions about integrating Accept Language Parser MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Accept Language Parser tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

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