How to Use the Mattermark MCP in CrewAI

Q: How do I configure CrewAI to use the Mattermark MCP Server?

Pass the server URL directly into the mcps list when initializing your CrewAI agents. For complex setups, use MCPServerHTTP from crewai.mcp to filter which agents get access to specific tools.

Q: Can I restrict specific CrewAI agents from searching Mattermark funding rounds?

Yes, you can use toolfilter within the MCPServerHTTP configuration to restrict access. This ensures your research agent can use searchcompanies while keeping financial tools restricted to your analyst agent.

Q: How do CrewAI agents share Mattermark company details during a run?

CrewAI uses shared memory, meaning once one agent calls getcompanydetails, the resulting firmographic data is available to other agents in the crew. This prevents duplicate API calls and keeps the team aligned.

Run autonomous multi-agent crews to research startups and analyze venture capital rounds with CrewAI.

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Connect Mattermark MCP to CrewAI

Create your Vinkius account to connect Mattermark 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 Mattermark with CrewAI

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Collaborative Market Mapping

`search_companies` enables your research agent to find startups in target sectors. A separate analysis agent then takes those companies and runs `list_similar_companies` to map out the entire competitor set. CrewAI's shared memory allows these agents to pass context back and forth. The analyst agent updates the researcher with newly discovered competitors, creating a deep market map without human intervention.

Autonomous Deal Due Diligence

`get_company_funding_rounds` pulls the entire capitalization history for a target startup. Your financial agent parses this history, while a news agent concurrently runs `get_company_news` to flag regulatory issues or product launches. This multi-agent setup processes data in parallel, saving hours of manual spreadsheet work. The agents compile their findings into a single investment memo automatically.

Sourcing Syndicates via CrewAI and MCP Server

`list_investors` identifies active venture funds that match a startup's stage and sector. Your partner agent then uses `get_investor_details` to analyze their past co-investments and board seats. By using an MCP Server, your CrewAI agents access real-time investor data directly. The crew operates autonomously, escalating only when they find a matching lead investor.

Setup guide

Set up Mattermark 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 Mattermark tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Mattermark 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="Mattermark Analyst",
        goal="Access and analyze Mattermark data via MCP.",
        backstory="Expert analyst with direct Mattermark access.",
        tools=mcp_tools,
    )

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

Pass the server URL directly into the `mcps` list when initializing your CrewAI agents. For complex setups, use `MCPServerHTTP` from `crewai.mcp` to filter which agents get access to specific tools.

Yes, you can use `tool_filter` within the `MCPServerHTTP` configuration to restrict access. This ensures your research agent can use `search_companies` while keeping financial tools restricted to your analyst agent.

CrewAI uses shared memory, meaning once one agent calls `get_company_details`, the resulting firmographic data is available to other agents in the crew. This prevents duplicate API calls and keeps the team aligned.

The integration supports standard stdio, SSE, and Streamable HTTP transports. You can deploy your agents anywhere and connect to the managed MCP server via a single secure URL.

All data passing through the server is protected by a zero-trust architecture. The startup metrics and employee counts fetched via the API are processed in ephemeral sandboxes and never written to disk.

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