How to Use the HealthCare.gov MCP in AutoGen

Q: Are the dental plans in a separate API?

No, the searchplans tool can search for both health and dental plans. You specify the plan type in the JSON input for the tool. Your agents can be programmed to run the search for each type.

Assemble teams of AutoGen agents that debate, verify, and converge on the best HealthCare.gov plan for a user's needs.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect HealthCare.gov MCP to AutoGen

Create your Vinkius account to connect HealthCare.gov 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.

GDPR Free for Subscribers

Setup HealthCare.gov with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Simulate Cost vs. Coverage Debates

Assign different roles to your agents and let them argue. A 'Cost-Cutter' agent can use `search_plans` to find plans with the lowest premiums. A 'Risk-Manager' agent can then use `get_plan_details` to find that those same plans have huge deductibles, and argue for a more balanced choice. This conversational approach surfaces the trade-offs that a single agent might miss. The final recommendation is stronger because it has survived criticism from another agent with a different priority.

Use Agents for Collaborative Research

Break down complex research tasks among multiple AutoGen agents. One agent can be responsible for user interaction, asking for their ZIP code and prescriptions. A second 'Researcher' agent can then take that information and use the `get_counties_by_zip`, `search_drugs`, and `search_plans` tools. A third 'Verifier' agent could then be tasked with double-checking the top 3 plans using `get_drug_coverage` and `get_provider_coverage`. The final answer is a product of their collaboration, with each step handled by a specialist agent.

Delegate API Interaction to a Tool-Using Agent

Keep your primary agents focused on logic, not API calls. You can create a dedicated `AssistantAgent` whose only job is to execute tool calls from this MCP server. Other agents in the group can then request actions like "find plans in Austin" or "check if Dr. Smith is in-network." The `McpToolAdapter` handles the schema translation automatically. This separates the high-level conversation from the low-level mechanics of calling the HealthCare.gov API, making your agent group easier to build and debug.

Setup guide

Set up HealthCare.gov 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 HealthCare.gov 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="HealthCare.gov_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent HealthCare.gov data")
print(result.messages[-1].content)

Get your connection token →

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="HealthCare.gov_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent HealthCare.gov data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by HealthCare.gov. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect HealthCare.gov now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about HealthCare.gov MCP in AutoGen

You'd set up a conversation. A UserProxy agent would get the doctor's name and ZIP code. A 'Finder' agent would use `search_plans` and `search_providers`. A 'Verifier' agent would then use `get_provider_coverage` to check each plan, and the agents would discuss the results to find the cheapest one that passes verification.

Absolutely. You can give two plan IDs to an agent group. One agent could call `get_plan_details` for both, and another agent could be tasked with summarizing the key differences in premiums, deductibles, and out-of-pocket maximums in plain English.

It's about modeling the decision process. Choosing a health plan involves competing priorities—cost, coverage, risk. By having different AutoGen agents represent these priorities and debate, the system is more likely to find a balanced, well-reasoned solution than a single agent optimizing for one variable.

No, the `search_plans` tool can search for both health and dental plans. You specify the plan type in the JSON input for the tool. Your agents can be programmed to run the search for each type.

The conversation happens within your agent environment. The MCP server only sees individual, stateless tool calls for data like plan details or provider lists. The context of your conversation—the user's needs, the agents' debate—is not shared with the server. Vinkius just secures the transport for each isolated API call.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript