Resemble AI MCP Server for CrewAIGive CrewAI instant access to 16 tools to Add Watermark, Create Clip, Create Project, and more

Name: Resemble AI
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

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The Resemble AI MCP Server for CrewAI is a standout in the Image Video category — giving your AI agent 16 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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+ other MCP clients

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Resemble AI Specialist",
    goal="Help users interact with Resemble AI effectively",
    backstory=(
        "You are an expert at leveraging Resemble 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 Resemble AI "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 16 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 Resemble AI MCP Server

Connect your Resemble AI account to any AI agent to generate, manage, and protect high-fidelity synthetic speech through natural conversation.

When paired with CrewAI, Resemble AI becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Resemble AI 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

Voice Synthesis — Create high-quality audio clips from text using custom or system voices with full SSML support.
Speech-to-Speech — Transform source audio into a target voice while preserving the original emotion, intonation, and timing.
Project Organization — List, create, and manage projects to keep your audio assets and clips organized.
Voice Management — List available voices, create new custom voice profiles, and manage training recordings.
AI Safety & Security — Detect deepfakes in audio files and apply or verify digital watermarks to ensure content authenticity.

update

Update clip on Resemble AI

Update an existing clip

verify

Verify watermark on Resemble AI

Verify a watermark in an audio file

Connect Resemble AI to CrewAI via MCP

Follow these steps to wire Resemble AI into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind 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 16 tools from Resemble AI

Why Use CrewAI with the Resemble AI MCP Server

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

Resemble AI + CrewAI Use Cases

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

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

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

Example Prompts for Resemble AI in CrewAI

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

"List all my Resemble AI projects and their UUIDs."

"Create a new audio clip in project proj_123 saying 'Welcome to the future of voice' using voice voice_789."

"Analyze this audio URL to see if it's a deepfake: https://example.com/audio.mp3"

Troubleshooting Resemble AI MCP Server with CrewAI

Common issues when connecting Resemble AI to CrewAI through 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.

Resemble AI + CrewAI FAQ

Common questions about integrating Resemble 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.