How to Use the Bland AI MCP in CrewAI

Q: Can I limit which CrewAI agents use Bland AI?

Yes. Use toolfilter in CrewAI's HTTP client to expose tools like sendcall only to your outbound specialist agent.

Q: How do CrewAI agents share Bland AI transcripts?

Agents use shared memory to pass the raw text from getcalldetails. The analyst agent reads it to update the team's state.

Q: Can a CrewAI agent hang up a Bland AI call?

Yes. A moderator agent can invoke endcall if the transcript indicates the user wants to unsubscribe.

Q: How do I configure the MCP Server in CrewAI?

Pass the Vinkius URL directly into the agent's mcps array. CrewAI handles the connection over HTTP automatically.

Q: How is the audio data from Bland AI protected?

Audio links retrieved through getrecording are processed entirely in memory. Vinkius uses ephemeral execution blocks that destroy all voice data immediately after the agent finishes reading it.

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Connect Bland AI MCP to CrewAI

Create your Vinkius account to connect Bland AI 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 Bland AI with CrewAI

Ask AI about this MCP

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Claude

Perplexity

Coordinate agent teams with this MCP Server

The `send_call` tool allows a phone agent in your crew to dial out and talk to real people. One agent can place the call while another prepares the script. This setup lets you build autonomous support desks. Your crew coordinates background tasks so the caller gets accurate answers in real-time.

Evaluate phone transcripts across your crew

The `analyze_call` tool lets a quality assurance agent review whether a call met its goals. It checks the conversation details without human intervention. The crew passes the output of `get_call_details` to a writer agent. That agent then drafts a summary and logs it in your database.

Orchestrate high-volume call campaigns

The `send_batch` tool lets your manager agent dispatch hundreds of phone calls at once. The crew handles the scale effortlessly. A monitoring agent uses `get_batch` to watch the progress of the queue. If it spots a high failure rate, it triggers `end_call` to stop the campaign.

Setup guide

Set up Bland AI 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 Bland AI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Yes. Use `tool_filter` in CrewAI's HTTP client to expose tools like `send_call` only to your outbound specialist agent.

Agents use shared memory to pass the raw text from `get_call_details`. The analyst agent reads it to update the team's state.

Yes. A moderator agent can invoke `end_call` if the transcript indicates the user wants to unsubscribe.

Pass the Vinkius URL directly into the agent's `mcps` array. CrewAI handles the connection over HTTP automatically.

Audio links retrieved through `get_recording` are processed entirely in memory. Vinkius uses ephemeral execution blocks that destroy all voice data immediately after the agent finishes reading it.

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