How to Use the Estimation Prover MCP in AutoGen

Q: How do I register the Estimation Prover MCP Server with an AutoGen agent?

You use the MCP tool adapter to convert the validateestimation tool schema. Then, pass the resulting tool list to your AssistantAgent constructor.

Q: Does the Estimation Prover support multi-agent debate on task complexity in AutoGen?

Yes, one agent can define the scope while another calls validateestimation to verify if the tasks are truly broken down into two-day chunks.

Force your AutoGen agents to debate timeline risks and validate estimates using this MCP Server.

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Connect Estimation Prover MCP to AutoGen

Create your Vinkius account to connect Estimation 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 Estimation Prover with AutoGen

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Perplexity

Debate timeline risks with multi-agent consensus

The `validate_estimation` tool gives your AutoGen agents a hard mathematical standard to debate against. One agent proposes a schedule, while another uses this tool to stress-test the assumptions. This friction kills the planning fallacy. The agents must negotiate until they produce a decomposed task list that passes the strict two-day limit rule.

Enforce objective buffers during agent negotiations

The `validate_estimation` tool prevents your developer agent from bullying your product agent into unrealistic timelines. Mathematically, the tool enforces a 20% to 60% buffer based on complexity. Your agents cannot agree on a final plan until the buffer is applied. This programmatic constraint ensures that human-like optimism does not infect your automated workflows.

Extract and validate hidden project assumptions

The `validate_estimation` tool forces your agents to explicitly list every assumption required for the estimate to hold. If your agents leave these assumptions unstated, the validation fails. This process exposes latent risks early in the conversation. Your agents must document these factors, giving you a clear picture of what could derail the project.

Setup guide

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

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

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

You use the MCP tool adapter to convert the `validate_estimation` tool schema. Then, pass the resulting tool list to your AssistantAgent constructor.

Yes, they use the tool to run objective checks on disputed timelines. The mathematical feedback forces the agents to reach a consensus based on data rather than negotiation.

Yes, one agent can define the scope while another calls `validate_estimation` to verify if the tasks are truly broken down into two-day chunks.

The agent receives a detailed error payload explaining which rule failed. The agent must then adjust the scope, buffers, or assumptions and try again.

Yes, all estimation scope details, historical precedents, and timeline data are processed within a secure, single-tenant V8 sandbox. Your sensitive planning metrics are never exposed to external networks or stored permanently.

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