How to Use the ChartHop MCP in AutoGen

Q: How do I register ChartHop MCP tools with AutoGen?

Install autogen-extmcp first. You use mcpservertools with StreamableHttpServerParams pointing to your Vinkius URL, then pass the resulting list to your AssistantAgent.

Give your AutoGen multi-agent debates direct access to ChartHop org structures via this MCP Server.

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Connect ChartHop MCP to AutoGen

Create your Vinkius account to connect ChartHop 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 ChartHop with AutoGen

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Claude

Perplexity

Multi-Agent Org Analysis

Sometimes HR decisions require multiple perspectives. You can configure a finance agent and an HR agent to debate restructuring by giving them both access to `get_organization_summary`. They do not just guess at the current setup. The agents pull `list_organization_departments` and actively argue over resource allocation based on the exact same live API data.

AutoGen MCP Server for Headcount

Planning budgets is a negotiation. One agent acts as a conservative CFO while another advocates for aggressive hiring, both pulling from `list_planning_scenarios`. The system forces them to reconcile their views. If the hiring agent suggests expanding a group, it checks `list_organization_teams` first, presenting real team sizes to the finance agent for approval.

Fact-Check Role Requirements

Job leveling debates get subjective quickly. When your agents disagree on a title's seniority, they can query `list_organization_jobs` to see the current roster of roles. A dedicated fact-checking agent steps in to pull `get_job_details`. It injects the actual compensation band and requirements into the chat, forcing the other agents to ground their argument in reality.

Setup guide

Set up ChartHop 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 ChartHop 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="ChartHop_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent ChartHop 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="ChartHop_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent ChartHop 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 ChartHop. 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 ChartHop MCP in AutoGen

Install `autogen-ext[mcp]` first. You use `mcp_server_tools` with `StreamableHttpServerParams` pointing to your Vinkius URL, then pass the resulting list to your `AssistantAgent`.

They share the tool definitions, but each agent decides when to invoke them. The `McpToolAdapter` automatically translates the responses so every participant in the chat sees the same org data.

The tool returns an error message directly into the conversation thread. The agent reading the failure will typically apologize to the group and try a different query, like running `list_organization_people` first.

Yes, the package handles both stdio and Streamable HTTP. Just drop your Vinkius endpoint token into the configuration and the connection handles the rest.

Security is baked into the infrastructure. When your agents debate numbers pulled from `get_job_details`, the request routes through a Vinkius V8 isolate that vanishes completely after returning the payload.

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