How to Use the JSON Path Query Engine MCP in AutoGen

Let your AutoGen agents debate and extract data from JSON payloads using the JSON Path Query Engine.

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Connect JSON Path Query Engine MCP to AutoGen

Create your Vinkius account to connect JSON Path Query Engine to AutoGen 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 AutoGen

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Consensus-driven data extraction

Multiple AutoGen agents can call the `query_json` tool to verify data points during a debate. One agent might query a price, while another checks the status field. They reach a conclusion based on the actual output of the tool. This prevents agents from guessing at values inside complex structures.

Agentic control of JSONPath

Your AutoGen assistants can adjust their query patterns based on what the `query_json` tool returns. If a path fails, they can propose a different one. This creates a self-correcting loop for data retrieval. Your agents handle the heavy lifting of navigating nested objects.

Standardized tool definitions in AutoGen

The MCP server automatically provides the tool schema to your AutoGen agents. There is no manual configuration required to get the agents talking to the engine. You just define the server and the agents take it from there. It cuts down on the setup time for your multi-agent architecture.

Setup guide

Set up JSON Path Query Engine MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes JSON Path Query Engine tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="JSON Path Query Engine_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent JSON Path Query Engine data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="JSON Path Query Engine_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent JSON Path Query Engine data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about JSON Path Query Engine MCP in AutoGen

Configure your AssistantAgent with the MCP tool adapter. It detects the `query_json` tool and makes it available to the agent group.

Yes. Once the server is registered, any agent in your conversation flow can invoke the tool to get data.

It provides consistent, deterministic output based on your JSONPath expressions. This gives your agents a solid foundation for their deliberations.

The tool returns an error message if the path is invalid. Your agents can read this response and try a different query.

The server runs in a strictly isolated environment. It only touches the JSON payload you provide and keeps no persistent record of your system's data.

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