How do I start a video upload?

Use the `create_video_upload` tool, which will create the asset in your specified collection and provide a temporary `upload_url` for your file.

Can I update the thumbnail for a video already uploaded?

Yes! The `update_video_thumbnail` tool allows you to specify a time offset in seconds. Gumlet will extract that specific frame and set it as the new thumbnail.

Does it support viewing stats and bandwidth usage?

The `get_video_analytics` tool provides detailed viewing metrics and bandwidth consumption data for any video ID in your account.

INSTANT PLUG AND PLAY

Gumlet MCP Server for CrewAIGive CrewAI instant access to 12 tools to Create Collection, Create Video Upload, Delete Video, and more

Name: Gumlet
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 12 Tools Framework

Connect your CrewAI agents to Gumlet through Vinkius, pass the Edge URL in the `mcps` parameter and every Gumlet 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

Open in ChatGPT Open in Claude Open in Perplexity

The Gumlet app connector for CrewAI is a standout in the Image Video 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="Gumlet Specialist",
    goal="Help users interact with Gumlet effectively",
    backstory=(
        "You are an expert at leveraging Gumlet 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 Gumlet "
        "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)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Gumlet MCP Server

Connect your Gumlet account to any AI agent and take full control of your video hosting and image optimization workflows through natural conversation.

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

Video Lifecycle — Manage the complete video lifecycle from creating new uploads and retrieving metadata to monitoring transcoding status
Media Organization — Create and manage collections/folders programmatically to maintain a structured media library
Visual Control — Automate thumbnail updates by selecting specific video frames or time offsets for perfect visual representation
Optimization Insights — Monitor real-time video analytics, viewing metrics, and bandwidth usage for every asset in your account
Image Source Management — List and manage image optimization sources and organization users to ensure high-fidelity delivery

The Gumlet 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 Gumlet tools available for CrewAI

When CrewAI connects to Gumlet through Vinkius, your AI agent gets direct access to every tool listed below — spanning video-hosting, image-optimization, cdn-delivery, 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.

create_collection

Add new folder

create_video_upload

Upload new video

delete_video

Remove video asset

get_account_info

Get profile details

get_video_analytics

Check video stats

get_video_details

Check video status

list_image_sources

List image optimized sources

list_org_users

List team members

list_video_collections

List folders

list_videos

List video assets

list_webhooks

Get active webhooks

update_video_thumbnail

Set thumbnail offset

Connect Gumlet to CrewAI via MCP

Follow these steps to wire Gumlet 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 Gumlet

Why Use CrewAI with the Gumlet MCP Server

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

Gumlet + CrewAI Use Cases

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

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

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

Example Prompts for Gumlet in CrewAI

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

"Create a new video upload in collection 'col_123' titled 'Annual Report 2026'."

"Check the transcoding status of video 'asset_987'."

"Show me the viewing stats for my latest product video."

Troubleshooting Gumlet MCP Server with CrewAI

Common issues when connecting Gumlet 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.

Gumlet + CrewAI FAQ

Common questions about integrating Gumlet 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.