How to Use the Estimation Prover MCP in Pydantic AI

Bring strict runtime validation to your project timelines by pairing Pydantic AI with the Estimation Prover.

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Connect Estimation Prover MCP to Pydantic AI

Create your Vinkius account to connect Estimation Prover to Pydantic AI 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 Pydantic AI

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Type-Safe Timeline Verification in Pydantic AI

The `validate_estimation` tool acts as a strict schema validator for your agent's project planning outputs. It forces your Pydantic AI agent to output structured estimation data that complies with strict decomposition rules. If the agent attempts to return a timeline with tasks larger than two days, the MCP Server triggers a validation error. This forces an immediate runtime failure, stopping bad estimates before they reach your database.

Verifying Assumptions and Historical Evidence

The `validate_estimation` tool demands that every estimate include a specific, verifiable historical precedent and a list of active assumptions. It rejects any plan that relies on vague hand-waving or missing contextual data. Because Pydantic AI validates every response at runtime, your agent cannot bypass these requirements. The model must provide structured proof of past performance, or the entire execution block fails loudly.

Rigid Buffer Enforcement for Novel Codebases

The `validate_estimation` tool automatically calculates and enforces a contingency buffer based on task complexity. It injects a hard minimum of 20% for familiar work and up to 60% for novel system integrations. This mathematical guardrail prevents your agent from generating overly optimistic delivery dates. The tool guarantees that every timeline written to your files has been padded against the planning fallacy.

Setup guide

Set up Estimation Prover MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "estimation-prover-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Estimation Prover tools.",
)

result = await agent.run("List recent Estimation Prover transactions")
print(result.output)

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Common questions about Estimation Prover MCP in Pydantic AI

Install the required package and initialize the toolset with your Vinkius MCP Server endpoint. Pass the toolset directly into your Agent constructor to expose the validation tool.

The tool throws a structured validation error that Pydantic AI catches at runtime. Your agent can read the specific failure reason, adjust the task breakdown, and retry the estimation automatically.

Yes, this MCP Server is completely model-agnostic. You can pair it with local models or commercial APIs, and it will still enforce the same strict estimation standards.

It uses reference class forecasting to defeat the planning fallacy. By forcing the agent to match the current task with a past project, it grounds the timeline in actual performance data.

All estimation parameters are processed inside an isolated, zero-trust V8 sandbox. Your sensitive historical metrics and task complexities are never stored or leaked to third parties.

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