How to Use the Bland AI MCP in LlamaIndex

Q: How do I filter tools for LlamaIndex?

Use the allowedtools filter during client initialization. This limits the agent to only the specific functions you want it to use.

Index your Bland AI call data into LlamaIndex to build knowledge-augmented voice agents that learn from past conversations.

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Connect Bland AI MCP to LlamaIndex

Create your Vinkius account to connect Bland 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 Bland AI with LlamaIndex

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Index Bland AI data for LlamaIndex

Convert call transcripts into a searchable knowledge base using `get_call_details`. LlamaIndex takes this raw data and turns it into vectors for your agent to query later. Stop relying on memory and start grounding your answers in reality. Your agent searches the index of past calls before making any new decisions.

Retrieve audio context for LlamaIndex

Pull raw MP3 links via `get_recording` and store them in your index. This allows your agent to reference specific audio moments when it needs to verify what happened. It makes your knowledge base complete. You gain the ability to search across thousands of calls for specific phrases or tone markers.

Automate phone operations with LlamaIndex

Use `send_call` to initiate contact and immediately index the result. This MCP Server lets your agent act on the data it finds in its own index. Your system stays synced. Everything that happens on the phone is added to your searchable index, creating a loop where the agent gets smarter with every call.

Setup guide

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

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Common questions about Bland AI MCP in LlamaIndex

You use the provided tools to fetch transcripts and store them in a vector index. This lets your agent perform semantic searches across all your previous phone interactions.

Absolutely, as the tool output is structured for easy indexing. Your agent fetches current call logs and adds them to the existing knowledge base.

Yes, once you index the call details, your agent can query the database to find patterns or specific information from historical calls.

Use the allowed_tools filter during client initialization. This limits the agent to only the specific functions you want it to use.

Your transcript data is encrypted in transit and at rest. Access control is managed through your unique endpoint token, ensuring only authorized agents read your call logs.

Use it with your favorite AI tools

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