How to Use the Serviceform MCP in CrewAI

Q: How do I structure Serviceform and crewai for autonomous operations?

You define specialized agents with roles. One agent runs listspaces to get the context, and another acts on that context, creating a self-managing operation.

Q: Can I use Serviceform and crewai to manage multiple lead sources?

You can assign a dedicated agent to handle listleads from different sources, letting the team collaborate on aggregating data into one master report.

Run autonomous teams of agents collaborating on Serviceform tasks using the crewai framework.

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…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Serviceform MCP to CrewAI

Create your Vinkius account to connect Serviceform 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 Serviceform with CrewAI

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Multi-Agent Lead Analysis

Assign one agent to research and run `list_leads`, while a second specialized agent analyzes that output. This structured approach ensures every piece of captured data is reviewed by an expert role. It's not just running tools; it’s making different agents collaborate on the same goal.

Autonomous Form Cataloging

You can create a crew where Agent A calls `list_forms` to get all schemas, and Agent B takes those schemas to generate documentation. This works in sequence or hierarchically, treating the tool output as input for the next agent. This is how you build operations that don't need constant human supervision.

Coordinated Chat History Retrieval

Define a 'Research Agent' to call `list_chats` with specific criteria, and then have an 'Action Agent' use that retrieved history to generate a summary report. The shared memory ensures the second agent knows exactly what the first one found. This coordination capability is key for complex reporting.

Setup guide

Set up Serviceform 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 Serviceform tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You define specialized agents with roles. One agent runs `list_spaces` to get the context, and another acts on that context, creating a self-managing operation.

Yes, absolutely. You can set up sequential or hierarchical execution where one agent's output becomes the next agent's input. It’s designed for complex operations.

You can assign a dedicated agent to handle `list_leads` from different sources, letting the team collaborate on aggregating data into one master report.

You pass the MCP URL directly in the agent configuration. The framework handles tool exposure and ensures that only necessary tools, like `list_chatbots`, are available to specific agents.

The server manages lead details and chat histories. Since the crew is running autonomously, monitoring which agent accesses what PII is crucial for compliance.

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