Can I query my Voiceflow Knowledge Base directly via AI?

Yes! Use the `query_kb` tool with your question. Your agent will trigger the Voiceflow RAG system and return the answer based on your uploaded documents.

How do I see the transcripts for a specific project?

Run the `list_transcripts` query with your Project ID. The agent will return a list of past conversation logs, which you can then inspect using `get_transcript`.

Is it possible to reset a user's session via AI?

Absolutely. Use the `delete_state` tool and provide the User ID. This will permanently clear the conversation history and variables for that specific session.

INSTANT PLUG AND PLAY

Voiceflow MCP Server for CrewAIGive CrewAI instant access to 12 tools to Delete State, Get Feedback, Get Project, and more

Name: Voiceflow
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 12 Tools Framework

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

Use with Your AI Agent

Ask AI about this App Connector for CrewAI

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The Voiceflow app connector for CrewAI is a standout in the Industry Titans category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Voiceflow Specialist",
    goal="Help users interact with Voiceflow effectively",
    backstory=(
        "You are an expert at leveraging Voiceflow 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 Voiceflow "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 12 available tools "
        "and what they can do."
    ),
)

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

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* 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 Voiceflow MCP Server

Connect your Voiceflow account to any AI agent and simplify how you build, test, and monitor your conversational assistants through natural language conversation.

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

What you can do

Agent Interaction — Send messages and trigger actions in your Voiceflow agents to test responses and flows instantly.
Knowledge Base (RAG) Control — Query your agent's KB directly for answers and list uploaded documents and tags.
State Management — Retrieve, update, or reset user conversation states and variables to debug complex logic.
Transcript Analysis — List and fetch full conversation logs for any project to monitor user interactions.
Operational Monitoring — Retrieve user feedback (upvotes/downvotes) and monitor project configurations in real-time.

The Voiceflow MCP Server exposes 12 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.

All 12 Voiceflow tools available for CrewAI

When CrewAI connects to Voiceflow through Vinkius, your AI agent gets direct access to every tool listed below — spanning conversational-ai, chatbot-design, rag-pipeline, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

delete_state

Reset user session

get_feedback

Get user feedback

get_project

Get project details

get_state

Get user conversation state

get_transcript

Get transcript details

interact

Send message to Voiceflow agent

list_kb_docs

List KB documents

list_kb_tags

List KB document tags

list_projects

List Voiceflow projects

list_transcripts

List conversation transcripts

query_kb

Ask the Knowledge Base

save_state

Update user state/variables

Connect Voiceflow to CrewAI via MCP

Follow these steps to wire Voiceflow into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

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 12 tools from Voiceflow

Why Use CrewAI with the Voiceflow MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Voiceflow 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 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

Voiceflow + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Voiceflow 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 Voiceflow, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Voiceflow 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 Voiceflow against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Voiceflow in CrewAI

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

"List all my Voiceflow projects."

"Ask my KB: 'What is the return policy for international orders?'"

"Show me the last 3 transcripts for the 'Customer Support Bot'."

Troubleshooting Voiceflow MCP Server with CrewAI

Common issues when connecting Voiceflow 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

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

Voiceflow + CrewAI FAQ

Common questions about integrating Voiceflow 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.