How to Use the CAMB.AI MCP in LlamaIndex

Q: What tools should I use for LlamaIndex indexing?

Focus on the status and result tools like getttsresult. These provide the final content that you need to store in your knowledge base for future queries.

Q: Can I filter tools when using this MCP server?

Yes, you can use the allowedtools filter to restrict which functions your agent can see. This helps keep your index clean and focused on the tasks that matter to your project.

Index your translated audio metadata into LlamaIndex for semantic search and retrieval.

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Connect CAMB.AI MCP to LlamaIndex

Create your Vinkius account to connect CAMB.AI 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 CAMB.AI with LlamaIndex

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Index CAMB.AI results in LlamaIndex

Run `create_tts` and send the output directly to your vector store. LlamaIndex treats these results as searchable knowledge, allowing you to query past audio tasks by content or language. You avoid hallucinations by grounding your agent's answers in the actual status data returned by `get_tts_result`. The agent references the index to see if a specific translation already exists before creating a new job.

Query voice assets using LlamaIndex

Map the output of `list_voices` into your knowledge base. Your LlamaIndex application can now perform semantic searches across your available voice profiles to find the best match for a specific project. This turns static API lists into active data. You can ask your agent which voices are available for a specific language, and it will query the indexed data rather than just guessing.

Automate RAG with this MCP Server

Connect your MCP Server tools to a FunctionAgent to build a RAG pipeline. When the agent needs to translate, it checks the index to see if it has the necessary language support from `list_source_languages`. This approach keeps your application aware of the API's capabilities. If a language isn't supported, the agent knows immediately because the data is indexed and available for retrieval.

Setup guide

Set up CAMB.AI 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 CAMB.AI 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 CAMB.AI tools.",
)
response = await agent.run("List recent CAMB.AI 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 CAMB.AI. 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 CAMB.AI MCP in LlamaIndex

Use the MCP client to wrap the tool spec and pass it to your FunctionAgent. This allows the agent to call the audio tools and index the results into your vector store.

It is built for it. By indexing the tool outputs, you create a searchable history of your audio jobs and voice configurations that your agent can query at any time.

We maintain a zero-trust boundary where your audio metadata is only accessed during the specific index update cycle. Your sensitive job information is never stored outside of your authorized environment.

Focus on the status and result tools like `get_tts_result`. These provide the final content that you need to store in your knowledge base for future queries.

Yes, you can use the allowed_tools filter to restrict which functions your agent can see. This helps keep your index clean and focused on the tasks that matter to your project.

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