How to Use the CometAPI MCP in LlamaIndex

Q: How do I index audio transcriptions from CometAPI into LlamaIndex?

Run transcribeaudiototext to convert the raw audio, then pass the resulting string to a LlamaIndex Document object. The document is then chunked and embedded into your vector database.

Q: Can LlamaIndex query live pricing using CometAPI MCP tools?

Yes, your agent calls getpricinginformation to pull current rates into its context window. This lets the query engine make real-time decisions about which model to use for synthesis.

Q: How do I set up the CometAPI MCP Server with LlamaIndex using BasicMCPClient?

Install llama-index-tools-mcp and initialize the client with the Vinkius URL. Then, convert the server tools to a list using McpToolSpec and pass them to your agent.

Q: Can I filter which tools my LlamaIndex agent can access?

Yes, you use the allowedtools filter when wrapping the MCP client. This restricts your agent to specific tools, like only allowing transcription while blocking image generation.

Q: Where does CometAPI store my generated images and transcriptions?

All generated assets from generateaiimage and text from transcriptions exist only in memory during the execution lifecycle. Vinkius runs these MCP tools in zero-trust sandboxes that wipe all session data the moment the tool returns its output.

Index live model usage, pricing data, and generated assets directly into your LlamaIndex vector store.

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Connect CometAPI MCP to LlamaIndex

Create your Vinkius account to connect CometAPI 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 CometAPI with LlamaIndex

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Claude

Perplexity

Index model outputs directly into LlamaIndex RAG pipelines

`create_ai_chat_completion` generates raw text responses that your agent immediately indexes into your document store. Repetitive API calls are prevented by converting every model interaction into searchable context for future queries.

Build audio-aware query engines with this MCP Server

`transcribe_audio_to_text` converts audio files into clean text documents that LlamaIndex parses and chunks. Natural language queries can then scan your audio archive, pulling specific quotes from hours of recorded meetings.

Ground agent decisions in live pricing and usage metrics

`get_pricing_information` retrieves current model rates to update your local metadata index. Cost-effective models are selected dynamically by your query engine for synthesizing final answers.

Setup guide

Set up CometAPI 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 CometAPI 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 CometAPI tools.",
)
response = await agent.run("List recent CometAPI data")

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Common questions about CometAPI MCP in LlamaIndex

Run `transcribe_audio_to_text` to convert the raw audio, then pass the resulting string to a LlamaIndex Document object. The document is then chunked and embedded into your vector database.

Yes, your agent calls `get_pricing_information` to pull current rates into its context window. This lets the query engine make real-time decisions about which model to use for synthesis.

Install `llama-index-tools-mcp` and initialize the client with the Vinkius URL. Then, convert the server tools to a list using `McpToolSpec` and pass them to your agent.

Yes, you use the `allowed_tools` filter when wrapping the MCP client. This restricts your agent to specific tools, like only allowing transcription while blocking image generation.

All generated assets from `generate_ai_image` and text from transcriptions exist only in memory during the execution lifecycle. Vinkius runs these MCP tools in zero-trust sandboxes that wipe all session data the moment the tool returns its output.

Use it with your favorite AI tools

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