How to Use the MCPFusion Developer Prover MCP in AutoGen

Q: Why does the MCPFusion Developer Prover tool reject raw Zod schemas in AutoGen-generated code?

Raw Zod schemas lack critical MCPFusion features like m.hidden() and m.timestamps(). The tool forces AutoGen agents to use defineModel() to preserve these architectural protections.

Let your AutoGen agents debate and enforce strict MCPFusion architectural patterns before deploying generated code.

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Connect MCPFusion Developer Prover MCP to AutoGen

Create your Vinkius account to connect MCPFusion Developer Prover 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 MCPFusion Developer Prover with AutoGen

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Enforce MVA Separation in AutoGen Multi-Agent Debates

The `validate_mcpfusion_implementation` tool acts as the ultimate arbiter when your AutoGen agents debate code quality. It analyzes the generated files to ensure that Model, View, and Agent layers remain strictly separated. If one agent writes coupled code, this MCP Server tool provides the concrete evidence needed to reject it. This setup stops poorly structured code from passing consensus, so it won't get deployed. Your AutoGen security and architecture agents can use the tool's feedback to force the writing agent to refactor its work before deployment.

Reject Raw Zod Schemas in AutoGen Workflows

The `validate_mcpfusion_implementation` tool scans generated code to detect raw `z.object()` declarations and replace them with `defineModel()`. AutoGen agents often default to standard Zod, which strips away critical features like automatic timestamps and hidden fields. This MCP tool flags these occurrences instantly. By enforcing `defineModel()`, your agents maintain data integrity across all generated modules. The tool forces the writing agent to define schemas using proper casts, preserving mass-assignment protection.

Force Presenter Egress in AutoGen Agent Code

The `validate_mcpfusion_implementation` tool checks that every data-returning tool designed by your AutoGen agents utilizes a Presenter. If an agent tries to return raw database objects, this tool blocks the code from being accepted. This prevents sensitive data leaks during multi-agent executions. Enforcing `createPresenter()` ensures that all output is clean and secure. The tool forces your AutoGen agents to filter sensitive fields and inject UI rendering rules before any data is returned.

Setup guide

Set up MCPFusion Developer Prover 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 MCPFusion Developer Prover 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="MCPFusion Developer Prover_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent MCPFusion Developer Prover 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="MCPFusion Developer Prover_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent MCPFusion Developer Prover data")
    print(result.messages[-1].content)

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Common questions about MCPFusion Developer Prover MCP in AutoGen

You can register the tool with a critic agent in your AutoGen group. The critic agent runs the validation tool against code proposed by other agents, rejecting any non-compliant implementations.

Yes, the server handles async validation calls seamlessly. Your AutoGen agents can run multiple validation checks in parallel without blocking the main conversation thread.

Raw Zod schemas lack critical MCPFusion features like `m.hidden()` and `m.timestamps()`. The tool forces AutoGen agents to use `defineModel()` to preserve these architectural protections.

The tool returns a structured error payload with concrete suggestions and actions. AutoGen agents discuss these suggestions and use them to refactor the code until it passes validation.

The server processes your TypeScript files and AST data entirely within an isolated memory sandbox. No source code or schema definition is ever written to disk or sent to an external service.

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