How to Use the Jinshuju / 金数据 MCP in AutoGen

Q: How do I register the Jinshuju / 金数据 MCP Server tools in AutoGen?

Use mcpservertools with your server URL to import the 10 tools. Pass this tool list directly to your AssistantAgent constructor.

Q: How do AutoGen agents collaborate on Jinshuju / 金数据 forms?

One agent can find forms using listforms, while another pulls fields via getformfields. They discuss the schema in a group chat before writing data.

Q: Can AutoGen handle automated entry cleanups in Jinshuju / 金数据?

Yes. An agent can monitor submissions using listentries, flag policy violations, and call deleteentry to remove them after consensus.

Build multi-agent AutoGen teams that debate and manage Jinshuju form entries in real time.

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Connect Jinshuju / 金数据 MCP to AutoGen

Create your Vinkius account to connect Jinshuju / 金数据 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 Jinshuju / 金数据 with AutoGen

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Collaborative Form Administration

This MCP Server exposes the `list_forms` tool to let your AutoGen coordinator agent find available data collection sheets. A separate analyst agent then runs `get_form_fields` to inspect the structure. The agents debate whether the schema matches your database requirements before making any changes. This consensus-driven approach prevents broken pipelines and invalid submissions.

AutoGen Multi-Agent Validation for Jinshuju

The `create_entry` tool is used by your execution agent to submit new customer records using this MCP tool. Before this happens, a validator agent checks the payload against the form rules. If errors are found, the validator instructs the executor to run `update_entry` with corrected values. This prevents dirty data from polluting your Jinshuju database.

Automated Data Auditing

The `get_entry` tool allows your audit agent to pull specific submissions for security reviews. It scans the content for sensitive data or potential spam. If the agent flags an entry as malicious, it negotiates with the admin agent to execute `delete_entry`. This keeps your database clean without manual human oversight.

Setup guide

Set up Jinshuju / 金数据 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 Jinshuju / 金数据 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="Jinshuju / 金数据_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Jinshuju / 金数据 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="Jinshuju / 金数据_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Jinshuju / 金数据 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 Jinshuju / 金数据. 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 Jinshuju / 金数据 MCP in AutoGen

Use mcp_server_tools with your server URL to import the 10 tools. Pass this tool list directly to your AssistantAgent constructor.

One agent can find forms using list_forms, while another pulls fields via get_form_fields. They discuss the schema in a group chat before writing data.

Yes. An agent can monitor submissions using list_entries, flag policy violations, and call delete_entry to remove them after consensus.

It supports both stdio and Streamable HTTP transports. Use the HTTP transport to connect your agents to the hosted server endpoint.

The server communicates with the Jinshuju API using secure HTTPS. It only reads and writes data like webhooks and entries, running inside a zero-trust sandbox that isolates your credentials.

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