Use Thermal Recovery Design with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. Simulate and optimize thermal enhanced oil recovery (EOR) projects.
Developed, maintained, and hosted by Vinkius.
MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED
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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.
Complete set · 4 capabilities
The complete Thermal Recovery Design capability set.
These are the exact actions your AI can choose when you ask it to work with Thermal Recovery Design.
01-04
4 capabilities in this set.
Part of 4 available through Thermal Recovery Design.
- 01
Analyze thermal losses
Quantifies the energy lost to the non-productive layers
- 02
Calculate steam injection rate
Determines the optimal rate of steam injection required to maintain a stable thermal front
- 03
Design well pattern
Determines the optimal spatial configuration and distance between wells
- 04
Estimate oil recovery
Predicts the total volume of oil that can be successfully recovered
One connector, every AI
Thermal Recovery Design works with the most popular AI clients.
These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.
Claude
ChatGPT
Gemini
Perplexity
Grok
Microsoft Copilot
Cursor
VS Code
Windsurf
JetBrains
Cline
LangChain
Vercel AI SDK
Lovable
Z.ai
Raycast
Qwen Code
Kimi Code
Le ChatBuilding your own app? The connector is yours to use.
You don't need a client to put Thermal Recovery Design to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.
Observed, not estimated
1014ms average. Fast in production.
Thermal Recovery Design is checked daily against the live service.
- Fastest day
- 1014ms
- Slowest day
- 1014ms
- 14-day trend
- Stable0%
Connect your client
One URL. Every client.
Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.
Preview access · not provider authentication
The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Thermal Recovery Design, so you can see the experience inside your AI.
It does not authenticate your account with Thermal Recovery Design. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
Thermal Recovery Design Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_6ryMJNs6b5eaveGID0QFh7aeOCAczbiqy6oxvOpc/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — Thermal Recovery Design capabilities are ready to use.
{
"mcpServers": {
"thermal-recovery-design-mcp": {
"url": "https://edge.vinkius.com/vk_preview_6ryMJNs6b5eaveGID0QFh7aeOCAczbiqy6oxvOpc/mcp"
}
}
}
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Step-by-step instructions for each client are in the guide. How to connect
Guided setup for Claude? link.label
FAQ
Questions Thermal Recovery Design owners ask.
- 01
How do I calculate the required steam injection rate?
You can use the calculate_steam_injection_rate capability by providing the oil viscosity, reservoir thickness, thermal conductivity, and your target temperature.
- 02
Can I predict the total oil recovery for my reservoir?
Yes, the estimate_oil_recovery capability predicts the total volume of oil that can be successfully recovered based on reservoir depth, thickness, pattern size, and injection rate.
- 03
How does this capability handle heat loss to surrounding rock?
The analyze_thermal_losses capability specifically quantifies energy lost to the overburden and underburden layers.
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