How to Use the Numbers API MCP in LlamaIndex

Q: How do I register the Numbers API with LlamaIndex?

Install the MCP tool package and initialize the BasicMCPClient with your Vinkius URL. Then, wrap it in a McpToolSpec and call totoollistasync() to pass the tools to your agent.

Q: Can I index data from the Numbers API into my LlamaIndex vector store?

Yes, you can run getmathfact or getyearfact and index the string outputs directly. This lets your LlamaIndex RAG pipeline retrieve verified historical and mathematical facts during semantic search.

Q: How does the Numbers API MCP Server prevent hallucinations in LlamaIndex?

It provides factual grounding by retrieving exact data via getdatefact instead of relying on the LLM's weights. Your LlamaIndex agent queries the API directly to get verified historical information.

Index real-time date and mathematical facts directly into your LlamaIndex vector stores.

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Connect Numbers API MCP to LlamaIndex

Create your Vinkius account to connect Numbers API 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 Numbers API with LlamaIndex

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Index math facts into LlamaIndex vector stores

`get_math_fact` retrieves raw mathematical properties that your LlamaIndex ingestion pipeline instantly indexes for semantic search. This tool transforms raw API responses into searchable document nodes, bridging the gap between live calculations and your static knowledge base. By using MCP tool spec configurations, you convert these live data streams into queryable vectors. Your RAG applications can then search past mathematical queries alongside your local PDF files.

Query historical dates using this MCP Server

This MCP Server lets your LlamaIndex agent fetch historical milestones using `get_year_fact` and `get_date_fact`. The returned facts are stored directly in your index, preventing your LLM from hallucinating historical timelines. You initialize the connection using `BasicMCPClient` to fetch real-world events. This keeps your index updated with accurate, verified dates without manual data entry.

Build hybrid RAG pipelines with trivia facts

Delivering unstructured trivia directly to your index queries, `get_trivia_fact` adds historical flavor to your data retrieval. Your LlamaIndex agent calls this tool to enrich document retrievals with engaging numeric trivia. Combined with `get_random_fact`, your search engine can serve unexpected connections during user queries. The agent handles the tool execution asynchronously, keeping search latency low.

Setup guide

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

Install the MCP tool package and initialize the `BasicMCPClient` with your Vinkius URL. Then, wrap it in a `McpToolSpec` and call `to_tool_list_async()` to pass the tools to your agent.

Yes, you can run `get_math_fact` or `get_year_fact` and index the string outputs directly. This lets your LlamaIndex RAG pipeline retrieve verified historical and mathematical facts during semantic search.

It provides factual grounding by retrieving exact data via `get_date_fact` instead of relying on the LLM's weights. Your LlamaIndex agent queries the API directly to get verified historical information.

Yes, LlamaIndex handles the tool calls asynchronously when you generate the tool list. This ensures that fetching multiple year or date facts does not block your main query pipeline.

Yes, all queries containing your specific numbers, years, and dates are executed within a secure V8 sandbox on Vinkius. The upstream API only receives the raw integers you look up, keeping your LlamaIndex application metadata completely isolated.

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