How to Use the Blip MCP in LlamaIndex

Index your Blip messages into LlamaIndex to query support history using semantic search.

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Connect Blip MCP to LlamaIndex

Create your Vinkius account to connect Blip 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 Blip with LlamaIndex

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Index Blip data for LlamaIndex

Turn your `list_threads` output into a searchable vector index. You ask questions about past customer issues and get answers based on actual message history. LlamaIndex treats your Blip conversation data as a source for RAG applications. You stop guessing what the customer said and start querying the record.

Filter Blip tools in LlamaIndex

Restrict the agent to only use `get_contact` or `list_tickets` by applying an allowed_tools filter. You maintain strict control over which data the agent accesses. This prevents the model from performing unintended actions on your Blip account. You define the boundaries of your agent's capabilities before it starts searching.

Ground Blip answers with LlamaIndex

Retrieve exact ticket details via `get_ticket` and inject them into your context window. Your agent provides answers anchored in real API data rather than hallucinated text. You build agents that understand the nuance of your support tickets. The integration keeps your knowledge base current with every API poll.

Setup guide

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

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Common questions about Blip MCP in LlamaIndex

Use the MCP tool spec to convert the server tools into a format the agent understands. You then pass the resulting list to your function agent.

The framework indexes the output of your tool calls into a vector store. This allows for fast semantic retrieval of past messages.

You can pull ticket data into your index and run queries against it. This makes it easy to find similar past issues for new support requests.

All communication occurs through an authenticated MCP server. We rotate your credentials and ensure the agent only reads what you permit.

The server processes contact names, phone numbers, and chat transcripts. This information is stored in your local vector index under your own security policy.

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