How to Use the Jestor MCP in AutoGen

Q: How do I register the Jestor MCP Server tools with AutoGen agents?

Install autogen-extmcp and use the streamable HTTP parameters to fetch the tools. Pass the resulting tool list directly to your AssistantAgent constructor.

Q: Can an AutoGen agent group debate before making changes to Jestor?

Yes. One agent can use getobject to check the schema, share it with the group, and wait for a consensus decision before calling the MCP tool.

Q: How do AutoGen agents handle Jestor webhook audits?

Run listwebhooks to gather all integration points. The agents then analyze the list to verify active endpoints and flag any anomalies in the conversation thread.

Q: What tool do I use to check the schema of a specific table?

Use getobject to pull the details and schema for a specific table. This tells you all the field types and relationships.

Let your AutoGen agents debate database actions and coordinate Jestor workflow updates through consensus.

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Setup Jestor with AutoGen

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Coordinate database audits using AutoGen agents

`list_objects` discovers all available tables in your account so your agent group can map out the data environment. This gives the coordinator agent a starting point to delegate tasks. The group can then call `list_records` to inspect specific entries. One agent can fetch the data while another validates the field values, ensuring complete agreement before any action.

Audit active backend automations via multi-agent debate

`list_workflows` pulls the logic behind your automated tasks so your agents can review system configurations. A security agent can flag risks while a developer agent proposes optimizations. Using `list_webhooks` alongside this allows the agents to audit external integrations. They compare active hooks against internal rules to spot discrepancies or broken endpoints.

Verify user access and permissions across teams

`list_users` returns names, emails, and IDs so your agents can check who owns specific records or workflows. It helps automate compliance checks by matching actions to active accounts. Pair this with `get_me` to verify the agent's own connection status and permission levels. This prevents authorization errors before the multi-agent team starts executing complex operations.

Setup guide

Set up Jestor 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 Jestor 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="Jestor_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install `autogen-ext[mcp]` and use the streamable HTTP parameters to fetch the tools. Pass the resulting tool list directly to your `AssistantAgent` constructor.

Yes. One agent can use `get_object` to check the schema, share it with the group, and wait for a consensus decision before calling the MCP tool.

Run `list_webhooks` to gather all integration points. The agents then analyze the list to verify active endpoints and flag any anomalies in the conversation thread.

Use `get_object` to pull the details and schema for a specific table. This tells you all the field types and relationships.

Yes. Vinkius executes the MCP server in a secure, ephemeral V8 container that isolates your user list and webhook configurations. Your data is processed in-memory and is never stored or cached on our servers.

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