How to Use the NeonCRM MCP in AutoGen
Create teams of AI agents that collaborate on NeonCRM tasks. Let them debate the best way to analyze donor data using AutoGen.
Works with every AI agent you already use
…and any MCP-compatible client
Connect NeonCRM MCP to AutoGen
Create your Vinkius account to connect NeonCRM 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.
Delegate NeonCRM Tasks to Agent Teams
This server gives your AutoGen agents the tools they need to work with NeonCRM data. You can build a team of agents that collaborate on a problem. For example, one agent can use `list_donations` to find major donors, while another uses `list_crm_events` to identify popular events. They don't just execute tasks; they talk to each other. The agents can discuss their findings and work together to come up with a strategy, like which donors to invite to a specific fundraising event. It's a way to automate complex analysis that would normally require a team meeting.
Build Consensus on Fundraising Strategy
AutoGen's strength is in multi-agent conversation, and these tools provide the fuel. Have a 'Growth Agent' look for patterns in `list_memberships` while a 'Retention Agent' analyzes data from `list_donations` to spot donors at risk of lapsing. They can then debate the best course of action. Should you focus on acquiring new members or retaining existing ones? The agents present their data-backed arguments, helping you make a more informed decision. This MCP server provides the facts for their debate.
A Dedicated MCP Server for AutoGen
Connecting AutoGen to NeonCRM is straightforward. The `autogen-ext` package includes an adapter that automatically finds and configures the tools from this server. You just provide the URL. This lets you focus on designing your agent conversations, not on writing boilerplate API code. Your agents get immediate, reliable access to all 10 read-only tools, from `get_account` to `list_custom_fields`, right out of the box.
Set up NeonCRM 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes NeonCRM tools and returns structured results.
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="NeonCRM_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent NeonCRM data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="NeonCRM_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent NeonCRM data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NeonCRM. 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 NeonCRM MCP in AutoGen
Use it with your favorite AI tools
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