How to Use the Bolt MCP in LlamaIndex

Q: Does this MCP Server support filtered indexing?

You can use the allowedtools filter to restrict which endpoints the agent accesses. This ensures your index only contains the data you want to query.

Index your payment history and order data into LlamaIndex to query your business performance with natural language.

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Connect Bolt MCP to LlamaIndex

Create your Vinkius account to connect Bolt 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 Bolt with LlamaIndex

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Index Bolt data for LlamaIndex

Pull your latest order details using `get_order_details` and feed the output into your vector store. This turns raw JSON responses into a searchable knowledge base for your RAG pipeline. You no longer need to manually parse transaction logs. The agent indexes the data, making it ready for complex queries about your sales volume or customer behavior.

Query merchant status via LlamaIndex

Use `get_merchant_status` to ground your agent's answers in real-time account data. When you ask about your current payout status, the agent pulls fresh data instead of relying on stale documents. This bridges the gap between static knowledge and live API data. You get answers that reflect the current state of your merchant account.

Audit transactions with MCP Server indexing

Run `list_transactions` and store the results in your index to perform trend analysis. Your RAG application can now compare today's performance against historical data it previously indexed. It provides a persistent memory of your financial activity. You query your own records using the same interface you use for your documentation.

Setup guide

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

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Common questions about Bolt MCP in LlamaIndex

Yes, the tools allow your agent to fetch live data which is then vectorized. This lets your RAG app answer questions about specific orders accurately.

Use the MCP tool spec to convert the server endpoints into functions your agent understands. Once added, the agent treats these functions as data sources for your index.

No, because the agent retrieves the live data through the tools before generating the answer. It uses the tool output as the primary source of truth.

You can use the allowed_tools filter to restrict which endpoints the agent accesses. This ensures your index only contains the data you want to query.

The data is stored locally in your own vector database after the agent retrieves it. Vinkius ensures that only your authenticated agent can access the raw transaction payloads.

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