How to Use the Bitso Exchange MCP in LlamaIndex

Q: How do I index my Bitso Exchange transaction history using LlamaIndex?

Use the listtrades and listledger tools to feed raw transaction data into a vector index. Your LlamaIndex agent can then run semantic search over your entire trading history.

Q: Can LlamaIndex execute trades on Bitso Exchange or is it read-only?

It can execute them directly. Your agent uses createorder to place trades and cancelorder to pull them, updating its local index immediately after the tool runs.

Q: How does a LlamaIndex agent check my deposit limits on Bitso Exchange?

The agent calls getaccountstatus to fetch your regulatory limits. It indexes this data so future trade decisions are automatically checked against your account's tier constraints.

Q: How do I get active trading books using this MCP Server?

Your agent runs listavailablebooks to fetch all active trading pairs. This gives you the exact book codes needed to place orders.

Index live Bitso Exchange order books and balances directly into LlamaIndex vector stores for grounded RAG querying.

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Connect Bitso Exchange MCP to LlamaIndex

Create your Vinkius account to connect Bitso Exchange 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 Bitso Exchange with LlamaIndex

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Ground LlamaIndex queries with live Bitso Exchange data

Turn raw exchange data from Bitso Exchange into a searchable knowledge base. Your LlamaIndex agent pulls active market books using `list_available_books` and indexes the output, allowing you to query current spreads without hallucination. By feeding `get_ticker` data straight into your index, your agent answers questions about current pricing using actual API facts. This keeps your financial research grounded in real-time market realities.

Build searchable transaction ledgers

Instead of digging through CSVs, let your LlamaIndex agent index your historical transactions using the Bitso Exchange MCP Server. The agent uses this MCP Server to call `list_ledger` and `list_trades` to build a vector index of your entire trading history. Once indexed, you can ask your agent natural language questions about your past performance. It will retrieve the exact transaction details from the vector store to back up its analysis.

Monitor balances and withdrawals via LlamaIndex RAG

Keep a constant, queryable record of your account state using our Bitso Exchange MCP Server. The agent checks your current holdings using `list_balances` and tracks outgoing transfers with `list_withdrawals` to keep your local index updated. When you ask how much capital is available for a trade, the agent queries its updated index. If limits are a concern, it queries `get_account_status` to ensure your plan aligns with exchange regulations.

Setup guide

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

Use the `list_trades` and `list_ledger` tools to feed raw transaction data into a vector index. Your LlamaIndex agent can then run semantic search over your entire trading history.

It can execute them directly. Your agent uses `create_order` to place trades and `cancel_order` to pull them, updating its local index immediately after the tool runs.

The agent calls `get_account_status` to fetch your regulatory limits. It indexes this data so future trade decisions are automatically checked against your account's tier constraints.

Your agent runs `list_available_books` to fetch all active trading pairs. This gives you the exact book codes needed to place orders.

Your financial data remains local to your execution environment. Vinkius executes the MCP Server within an ephemeral, zero-trust sandbox, ensuring your ledger flows and balances are never exposed to external servers during LlamaIndex operations.

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