Can I send a phone call with specific instructions using the agent?

Yes! Use the `send_call` action with the target phone number and a 'task' string describing exactly what the AI should say and do during the call.

How do I retrieve the transcript of a completed call?

Simply ask the agent to `get_transcript` and provide the Call ID. It will retrieve the full conversation log from the Bland AI engine.

Can I list all available voices before starting a call?

Yes. Use the `list_voices` tool to see all AI voices available in your Bland AI account, including their names and unique IDs.

Bland AI MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Bland AI
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Bland AI through the Vinkius — pass the Edge URL in the `mcps` parameter and every Bland AI tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Bland AI Specialist",
    goal="Help users interact with Bland AI effectively",
    backstory=(
        "You are an expert at leveraging Bland AI tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Bland AI "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Bland AI MCP Server

Connect your Bland AI account to any AI agent and orchestrate your automated phone call workflows through natural conversation.

When paired with CrewAI, Bland AI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Bland AI tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

What you can do

Outbound Automation — Send AI-powered phone calls with custom tasks and specific voices.
Call Monitoring — List recent calls and retrieve detailed metadata, including transcripts and analysis.
Agent Management — Access and manage your AI 'personas' (agents) used for different call scenarios.
Pathway Coordination — Retrieve and utilize complex conversation pathways for branching logic during calls.
Voice Discovery — List all available AI voices to find the perfect fit for your brand.
Operational Control — Stop active or scheduled calls instantly if needed.

The Bland AI MCP Server exposes 10 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Bland AI to CrewAI via MCP

Follow these steps to integrate the Bland AI MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Bland AI

Why Use CrewAI with the Bland AI MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Bland AI through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Bland AI + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Bland AI MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Bland AI for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Bland AI, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Bland AI tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Bland AI against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Bland AI MCP Tools for CrewAI (10)

These 10 tools become available when you connect Bland AI to CrewAI via MCP:

get_agent

Get details of a specific AI agent

get_call

Get details and metadata of a call

get_pathway

Get details of a specific pathway

get_transcript

Retrieve the transcript of a completed call

list_agents

List all AI agents

list_calls

List recent AI calls

list_pathways

List all conversation pathways

list_voices

List available AI voices

send_call

Send an AI phone call

stop_call

Stop an active or scheduled call

Example Prompts for Bland AI in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Bland AI immediately.

"Call +15550199 and ask if they are still interested in our pricing plan."

"List all AI agents in my account."

"Show the transcript for call call_998877."

Troubleshooting Bland AI MCP Server with CrewAI

Common issues when connecting Bland AI to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Bland AI + CrewAI FAQ

Common questions about integrating Bland AI MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.