How to Use the Nimbata MCP in CrewAI

Q: How do I load Nimbata tools into CrewAI?

Run pip install crewaitools. You can pass the endpoint URL directly into the mcps array parameter when defining your Agent.

Q: Can I restrict which Nimbata tools a CrewAI agent uses?

Yes. Use the MCPServerHTTP class from crewai.mcp and apply a toolfilter. You can give the analyst agent read-only access while letting the ops agent create sources.

Q: How do multiple CrewAI agents share Nimbata data?

CrewAI uses shared memory. If Agent A calls searchcalls and finds a specific session, Agent B can read that session ID from memory and fetch the recording.

Deploy autonomous agent teams in CrewAI to manage the Nimbata MCP Server and analyze marketing attribution.

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Connect Nimbata MCP to CrewAI

Create your Vinkius account to connect Nimbata 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 Nimbata with CrewAI

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Delegate Call Analysis to Agents

The `get_call_details` tool provides granular data on individual phone sessions. You assign this to a researcher agent in CrewAI, which constantly monitors incoming calls for duration and origin. When the researcher spots a high-value interaction, it passes the session ID to an analyst agent. The analyst then uses `get_call_recording` to pull the audio for review without human input.

CrewAI MCP Server Operations

The `get_source_report` tool feeds raw attribution data directly into your agent crew's shared memory. A reporting agent pulls this data to evaluate which ad campaigns actually made the phone ring. If a campaign underperforms, the moderator agent takes over. It can trigger `list_sources` to audit the current setup and flag misconfigured dynamic number pools for the marketing team.

Autonomous Campaign Setup

The `create_source` tool lets your operations agent spin up tracking numbers for new ad initiatives. The agent reads a campaign brief, decides it needs a new tracking pool, and provisions it immediately. It then verifies the setup using `get_number` to ensure the routing is correct. The entire crew coordinates to launch the tracking infrastructure while you focus on other work.

Setup guide

Set up Nimbata 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 Nimbata tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Run `pip install crewai[tools]`. You can pass the endpoint URL directly into the `mcps` array parameter when defining your Agent.

Yes. Use the `MCPServerHTTP` class from `crewai.mcp` and apply a `tool_filter`. You can give the analyst agent read-only access while letting the ops agent create sources.

CrewAI uses shared memory. If Agent A calls `search_calls` and finds a specific session, Agent B can read that session ID from memory and fetch the recording.

The agent reads the error message from the MCP Server and attempts to fix the parameters. If it cannot resolve the issue, it escalates to the manager agent.

The agents process raw MP3 call recordings and metadata. Authentication happens via a single endpoint token, meaning your core API credentials never touch the client machine.

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