Bring Interactive Video
to CrewAI
Learn how to connect Tolstoy to CrewAI and start using 6 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
What is the Tolstoy MCP Server?
Connect your Tolstoy interactive video account to any AI agent and simplify how you build personalized video experiences, manage your media library, and track engagement through natural conversation.
What you can do
- Video Management — List all uploaded videos and programmatically import new content via external URLs.
- Interactive Projects — Query and manage your branching video flows and personalized interactive experiences.
- Performance Tracking — Retrieve detailed analytics including plays, conversion rates, and revenue impact for your videos.
- Media Organization — List and oversee video folders to keep your marketing assets structured.
- Event Monitoring — List configured webhooks to ensure your real-time video notifications are active.
- Engagement Insights — Fetch high-level summaries of how users are interacting with your video content.
How it works
1. Subscribe to this server
2. Enter your Tolstoy API Key (found in your account settings)
3. Start managing your interactive videos from Claude, Cursor, or any MCP client
Who is this for?
- Marketing Managers — quickly check video conversion rates and manage interactive flows via simple AI commands.
- Content Creators — upload new videos and monitor engagement metrics directly from the workspace.
- E-commerce Owners — track the revenue impact of your shoppable interactive videos via the AI assistant.
Built-in capabilities (6)
Get performance metrics
List video folders
List interactive video projects
List your Tolstoy videos
List configured webhooks
Upload a new video to Tolstoy
Why CrewAI?
When paired with CrewAI, Tolstoy becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Tolstoy tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
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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
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter 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
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Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Tolstoy in CrewAI
Tolstoy and 3,400+ other MCP servers. One platform. One governance layer.
Teams that connect Tolstoy to CrewAI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 3,400+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Tolstoy in CrewAI
The Tolstoy 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. All 6 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Tolstoy for CrewAI
Every tool call from CrewAI to the Tolstoy MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I see the conversion rate for my interactive videos via AI?
Yes! Use the get_video_analytics tool. Your agent will retrieve detailed performance metrics, including plays and the percentage of users who converted through the interactive elements.
How do I import a new video into my library using the AI?
Use the upload_video action and provide the public URL of the video file. You can also specify an optional name for the video in your Tolstoy library.
Is it possible to list all my branching video projects?
Absolutely. Use the list_interactive_projects tool to retrieve a directory of all your interactive flows and branching video experiences.
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.
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.