How to Use the Finnhub MCP in LlamaIndex

Q: Can LlamaIndex query past Finnhub news searches?

Yes. When your agent calls getmarketnews, LlamaIndex stores the resulting articles in your document index. You can then run semantic search queries over those archived news items.

Q: How does LlamaIndex handle missing tickers in Finnhub?

The agent calls searchsymbols to find the closest match. If no match is found, LlamaIndex logs the empty result and avoids calling getcompanyprofile to prevent API errors.

Index real-time Finnhub market data directly into your LlamaIndex vector stores using this MCP Server.

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Connect Finnhub MCP to LlamaIndex

Create your Vinkius account to connect Finnhub 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 Finnhub with LlamaIndex

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Build a financial RAG index with this MCP Server

The `get_basic_financials` tool fetches raw balance sheet and income statement metrics to feed your LlamaIndex vector store. Your agent indexes these numbers on the fly, creating a searchable database of corporate performance metrics. This architecture prevents your RAG application from hallucinating financial figures during user queries. The system queries the index first, falling back on live tool execution if the requested ticker data is missing.

Index live market news into LlamaIndex

The `get_market_news` tool retrieves the latest headlines and articles across specific market categories. LlamaIndex ingests this text stream, splits it into nodes, and embeds them into your active vector database for immediate semantic search. Your query engine parses these fresh news embeddings alongside historical documents. This ensures your financial analysis remains grounded in both long-term company reports and today's breaking market events.

Resolve and index company profiles in LlamaIndex

The `get_company_profile` tool retrieves corporate metadata including sector, country, and share outstanding metrics. Your LlamaIndex agent uses this data to tag vector nodes, organizing your financial documents by industry and market cap. When your agent runs `search_symbols` to find a company, it indexes the search results to map colloquial names to official ticker symbols. This keeps your document index organized and easily searchable by non-technical users.

Setup guide

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

You use the LlamaIndex MCP tool spec to fetch live quotes via `get_stock_quote`. The agent converts the JSON payload into a document node and inserts it into your vector store for real-time querying.

Yes. When your agent calls `get_market_news`, LlamaIndex stores the resulting articles in your document index. You can then run semantic search queries over those archived news items.

The agent calls `search_symbols` to find the closest match. If no match is found, LlamaIndex logs the empty result and avoids calling `get_company_profile` to prevent API errors.

You pass the six tools to a LlamaIndex FunctionAgent. The agent uses `get_basic_financials` to pull valuation data and answers user questions using actual financial metrics instead of pre-trained weights.

Your financial metrics and ticker search histories remain inside your private LlamaIndex application. Vinkius executes the MCP tools in an ephemeral container, ensuring your API keys and query data are instantly destroyed after each call.

Use it with your favorite AI tools

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