How to Use the Option Pool Shuffle Simulator MCP in CrewAI

Deploy a CrewAI legal team to audit VC term sheets. Autonomous agents calculate equity dilution and negotiate better terms.

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Connect Option Pool Shuffle Simulator MCP to CrewAI

Create your Vinkius account to connect Option Pool Shuffle Simulator to CrewAI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Option Pool Shuffle Simulator with CrewAI

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Autonomous Term Sheet Audits

The `compare_shuffle_impact` tool gives your analyst agent the ability to quantify the exact difference between two option pool structures. One agent extracts the numbers from the PDF while another runs the math via the MCP. This is how you build a specialized negotiation crew. The system identifies the hidden cost of the shuffle and passes the delta to a strategy agent. Bottom line is your AI client spots the trap before the lawyers even wake up.

Model Founder Dilution with CrewAI

VCs structure deals to protect their own ownership percentage. Your financial agent uses `compute_pre_money_scenario` to determine the precise hit the founders take when the pool comes out of their slice alone. The crew shares this context in memory. A moderator agent watches the calculation and flags any scenario where founder control drops below a critical threshold. The operation runs entirely without human intervention.

Calculate Fair Equity Splits

Pushing back on standard terms requires hard data. Calling `compute_post_money_scenario` provides the exact figures needed to argue for shared dilution across all existing shareholders. You configure your communication agent to draft the counter-offer based on these outputs. The multi-agent pipeline handles the research, the math, and the response in one sequential execution.

Setup guide

Set up Option Pool Shuffle Simulator MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Option Pool Shuffle Simulator tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Option Pool Shuffle Simulator Analyst",
    goal="Access and analyze Option Pool Shuffle Simulator data via MCP.",
    backstory="Expert analyst with direct Option Pool Shuffle Simulator access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Option Pool Shuffle Simulator transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Option Pool Shuffle Simulator Analyst",
        goal="Access and analyze Option Pool Shuffle Simulator data via MCP.",
        backstory="Expert analyst with direct Option Pool Shuffle Simulator access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Option Pool Shuffle Simulator transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 Option Pool Simulator. 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 Option Pool Shuffle Simulator MCP in CrewAI

Install the crewai tools package via pip. Pass the endpoint URL directly into the mcps array when defining your agent.

Yes. Use the MCPServerHTTP class and apply a tool filter. You can give the financial agent access to the math while blocking the research agent.

The framework manages shared memory automatically. The analyst calculates the equity split and the writer agent uses those exact numbers to draft the email.

The connection supports standard input/output, server-sent events, and Streamable HTTP. You pick the transport that fits your deployment environment.

Your proposed share prices and unallocated option reserves remain completely private. The MCP performs stateless calculations without persisting any of your financial inputs to a database.

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