How to Use the NIST NVD MCP in LlamaIndex

Index live vulnerability data into your LlamaIndex RAG system for grounded security insights.

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Connect NIST NVD MCP to LlamaIndex

Create your Vinkius account to connect NIST NVD 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 NIST NVD with LlamaIndex

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Vectorize vulnerability intelligence

Convert NVD search results into searchable embeddings. By using `search_cpe_by_keyword`, you can push product-specific vulnerability data into your index for semantic discovery. Your RAG application now references live NVD data instead of relying on static documentation. It makes your agent smarter about specific product risks.

Ground responses in real-time data

Stop guessing about security status by letting your agent query the NVD directly. Tools like `get_cve_change_history` provide the exact timeline of a vulnerability's evolution. This gives your agent a factual basis for its answers. It pulls the latest data to ground its reasoning in reality.

Unified vulnerability knowledge base

Combine your private documents with public security data. Your index treats `list_cpe_matches` results as part of the total knowledge set available to your agent. This creates a single source of truth for your security team. You query your docs and the NVD in one go.

Setup guide

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

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Common questions about NIST NVD MCP in LlamaIndex

You call the tools to retrieve raw CVE or CPE data and then pass those results to your indexer. It turns the JSON output into a searchable knowledge object for your agent.

Once you index the tool outputs, yes. Your agent can perform semantic queries across your combined documents and vulnerability records.

It does. You map the server tools to your FunctionAgent, and the agent decides when to pull external data to supplement its internal knowledge.

You trigger a refresh by calling the search tools again and updating your vector store. The server provides the raw data, and your application handles the update cycle.

It accesses technical vulnerability reports, including CVE IDs, CVSS scores, and CPE product strings. This sensitive security metadata is handled via secure MCP transport.

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