How to Use the DeckMatch MCP in CrewAI

Q: How do I configure DeckMatch tools in CrewAI?

Pass your Vinkius endpoint directly into the mcps array when defining your Agent. For more granular control, import MCPServerHTTP and use a toolfilter to restrict which agent gets which capability.

Deploy a CrewAI research team to autonomously evaluate startup pitches using the DeckMatch tools to source, analyze, and draft memos.

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Connect DeckMatch MCP to CrewAI

Create your Vinkius account to connect DeckMatch to CrewAI 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 DeckMatch with CrewAI

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Multi-Agent Deal Flow Operations

One agent cannot handle deep diligence alone. You can assign a Junior Analyst agent to connect to this MCP Server and run `submit_pitch_deck` while a Senior Partner agent waits to review the output using shared memory. The junior bot pulls `get_deck_analysis` and summarizes the market size. The senior bot reads that summary, queries `list_enrichment_sources` for validation, and decides if the startup deserves a meeting.

Autonomous Market Mapping with CrewAI

Building a competitive map requires digging through hundreds of companies. A dedicated research agent can loop through `search_startups_semantically` to find every competitor mentioned in a new deck. It compiles a list of rivals and passes them to a data entry agent. That second agent uses `tag_submission` to categorize the original deal based on the competitors the first agent found, all without human input.

Supervised Memo Generation via MCP Server

Writing investment memos needs oversight. You can set up a hierarchical crew where a writer agent drafts the document using `generate_investment_memo` and submits it to a manager agent. The manager agent reviews the draft against the raw data from `get_submission_details`. If the writer missed a key risk factor, the manager kicks it back for a rewrite before saving the final version.

Setup guide

Set up DeckMatch 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 DeckMatch tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent DeckMatch 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 →

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="DeckMatch Analyst",
        goal="Access and analyze DeckMatch data via MCP.",
        backstory="Expert analyst with direct DeckMatch access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent DeckMatch 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 DeckMatch. 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 DeckMatch MCP in CrewAI

Pass your Vinkius endpoint directly into the `mcps` array when defining your Agent. For more granular control, import `MCPServerHTTP` and use a `tool_filter` to restrict which agent gets which capability.

You can build a background crew that polls for new deals. A monitor agent runs `list_submissions` every hour and wakes up a diligence agent when a new startup appears.

The tools fit perfectly into a manager-worker setup. The manager delegates the data gathering tasks to worker agents and synthesizes their findings into a final decision.

The Python package supports stdio, SSE, and Streamable HTTP. Vinkius handles the server-side infrastructure, so you just point your HTTP client at the provided URL.

Revenue run rates and customer acquisition costs never touch a shared database. Vinkius processes your crew's API calls inside an isolated, ephemeral sandbox that vanishes the moment the connection drops.

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