How to Use the Agora MCP in LlamaIndex

Q: Can LlamaIndex search through past Agora recording sessions?

Yes, by indexing the output of querycloudrecording into your vector store. Your agent then searches past recordings semantically to find specific session files.

Q: Does this MCP Server support LlamaIndex resource loading for Agora schemas?

Yes, set includeresources=True to let LlamaIndex directly read Agora schema data. This gives your agent deep context on how to query usage statistics.

Q: How do I start a cloud recording with this setup?

Your agent first runs acquirecloudrecording to get a resource ID. It then passes that ID to startcloudrecording to begin writing the video stream to your cloud storage.

Q: Where does my Agora billing and usage data go?

All data fetched via getusagestats remains local to your LlamaIndex runtime. Vinkius operates as a secure, zero-trust gateway that hosts this MCP Server and never caches your Agora usage payloads or credentials.

Index live Agora call metrics and whiteboard data directly into LlamaIndex vector stores for real-time RAG operations.

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Connect Agora MCP to LlamaIndex

Create your Vinkius account to connect Agora to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Agora with LlamaIndex

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Index Agora interactive whiteboard data in LlamaIndex

`list_whiteboard_rooms` pulls active whiteboard session details straight into your LlamaIndex pipelines. The framework stores this raw room data as document nodes, making current collaboration sessions searchable. Your agent queries this index to find patterns in whiteboard usage over time. This turns ephemeral room metadata into permanent semantic knowledge without writing custom ETL scripts.

Context-aware Agora project management via MCP Server

`list_projects` pulls all your active Agora environments and feeds their current configurations into LlamaIndex. Your agent uses `get_project_details` to resolve specific environment variables when building RAG prompts. By indexing these configurations, your agent never hallucinates project IDs when you ask it to check usage. It pulls the exact ID from the local vector index before making any API requests.

Ground LlamaIndex queries in live Agora usage metrics

`get_usage_stats` fetches real-time bandwidth and minutes consumed across your Agora apps. LlamaIndex indexes these data points to answer natural language questions about your billing and consumption trends. Combine this with `list_active_calls` to build a live dashboard that answers questions about current server load. The agent retrieves the latest stats, updates the index, and generates accurate reports.

Setup guide

Set up Agora MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Agora MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Agora tools.",
)
response = await agent.run("List recent Agora data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Agora. 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.

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Common questions about Agora MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate the BasicMCPClient. Wrap it in McpToolSpec and call to_tool_list_async() to hand the tools over to your FunctionAgent.

Yes, by indexing the output of query_cloud_recording into your vector store. Your agent then searches past recordings semantically to find specific session files.

Yes, set include_resources=True to let LlamaIndex directly read Agora schema data. This gives your agent deep context on how to query usage statistics.

Your agent first runs acquire_cloud_recording to get a resource ID. It then passes that ID to start_cloud_recording to begin writing the video stream to your cloud storage.

All data fetched via get_usage_stats remains local to your LlamaIndex runtime. Vinkius operates as a secure, zero-trust gateway that hosts this MCP Server and never caches your Agora usage payloads or credentials.

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