How to Use the Salespanel MCP in CrewAI

Q: How does CrewAI manage Salespanel data across agents?

CrewAI uses shared memory to pass the results of getvisitor or getcompany between your researchers and closers.

Q: Are Salespanel tools effective for CrewAI monitoring?

Yes, using listactivityfeeds allows your agents to act as a 24/7 watch team for your highest-value prospects.

Deploy a specialized sales crew. Use CrewAI to research and qualify leads using the Salespanel MCP Server.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Salespanel MCP to CrewAI

Create your Vinkius account to connect Salespanel 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 Salespanel with CrewAI

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Role-based lead research in CrewAI

Assign a Researcher agent to monitor `list_anonymous_visitors`. It finds high-value targets and passes them to a Moderator agent for final approval. This keeps your human team out of the weeds. The agents work in sequence, building a shared memory of the lead's intent.

Monitor activity feeds with Salespanel MCP Server

Your monitoring agent keeps a constant watch on `list_activity_feeds`. It flags significant events like pricing page visits or document downloads. This keeps your crew ready to act. Instead of waiting for a ping, the agents proactively prepare the context for your sales team.

Coordinate autonomous sales ops in CrewAI

Create a team that handles lead enrichment from start to finish. One agent calls `get_contact_details` while another verifies the company info via `get_company`. This creates a production-grade operation. Your agents collaborate to ensure the data is accurate before any outreach happens.

Setup guide

Set up Salespanel 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 Salespanel tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Salespanel 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 →

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