How to Use the JSON Path Query Engine MCP in LlamaIndex

Q: How do I integrate the JSON Path Query Engine into LlamaIndex?

Use the MCP tool spec to wrap the queryjson function. This makes it available to your LlamaIndex agents as a searchable tool.

Index raw API responses into LlamaIndex by extracting only the relevant data points with our MCP Server.

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Connect JSON Path Query Engine MCP to LlamaIndex

Create your Vinkius account to connect JSON Path Query Engine 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 JSON Path Query Engine with LlamaIndex

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Filter data before indexing

Use the `query_json` tool to strip away irrelevant metadata from large JSON blobs. You only index the data that actually informs your RAG application. Smaller indices mean faster retrieval and fewer hallucinations. You gain control over exactly what enters your vector store.

Grounded LlamaIndex queries

Point your agent at the `query_json` tool to pull live data when a user asks a specific question. The extracted result acts as a source of truth for your index. This keeps your knowledge base current without needing to re-index the entire dataset. You simply fetch the latest state on demand.

Unified data access for LlamaIndex

The JSON Path Query Engine serves as a bridge between your raw API sources and your LlamaIndex tools. You define the path, and the server handles the extraction. It works seamlessly with your existing FunctionAgent setup. You get a clean, predictable output format every single time.

Setup guide

Set up JSON Path Query Engine 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 JSON Path Query Engine 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 JSON Path Query Engine tools.",
)
response = await agent.run("List recent JSON Path Query Engine data")

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Common questions about JSON Path Query Engine MCP in LlamaIndex

Use the MCP tool spec to wrap the `query_json` function. This makes it available to your LlamaIndex agents as a searchable tool.

It works alongside them. You can use the tool to extract data from a fresh API call and then add that result to your index.

Absolutely. You can use filters and wildcards in your JSONPath expressions to grab specific subsets of data for your RAG pipeline.

By reducing the amount of irrelevant JSON text in your context. You feed the model only the specific data points it needs to answer the question.

The server processes your data in an ephemeral memory space. No raw user information or credentials are saved or logged after the request finishes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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