thermal-mcp-server
thermal-mcp-server is a Python-based MCP server exposing a physics/engineering model for liquid-cooled GPU thermal behavior. It estimates junction temperatures and hydraulic pressure drops, and can search for minimum coolant flow rates for a target junction temperature and compute rack-level thermal requirements for uniform GPU assumptions.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
The package description/docs do not mention auth, authorization scopes, or transport security (e.g., TLS) because it appears intended for local MCP usage. That implies higher risk if exposed over a network without an external security layer. Dependencies shown are fastmcp and pydantic; no CVE or audit info is provided. The README suggests computations only and does not discuss logging of secrets; secret-handling score is therefore moderate/unknown based on docs.
⚡ Reliability
Best When
You need engineering-first, explainable estimates for steady-state liquid cooling sizing and want an AI agent to call structured tools with validated test coverage.
Avoid When
You need precise plant-level pump/CDU sizing including manifold losses, transient effects, or flow distribution details without adding those effects elsewhere.
Use Cases
- • Estimate cold-plate junction temperatures for liquid-cooled GPUs given heat load, inlet temperature, coolant type, and flow rate
- • Size coolant flow rate to meet a junction temperature ceiling (e.g., CDU sizing inputs)
- • Compare coolant options (water vs. glycol blend) for thermal and hydraulic penalty
- • Estimate rack-level thermal outcomes and total flow requirement for N identical GPUs in series or parallel topology
- • Provide intermediate physics quantities (Re, Nu, convection coefficient, pressure drop) for engineering review
Not For
- • Accounting for manifold/header pressure losses (cold-plate ΔP only)
- • Mixed-SKU racks and non-identical GPU assumptions without per-GPU modeling
- • Transient thermal behavior (startup ramps, burst workloads, cooldown curves)
- • Accurate fluid property variation along the flow path (single-point properties assumed)
- • Flow maldistribution and imperfect parallel manifold behavior beyond uniform-flow assumptions
Interface
Authentication
The README shows running the server locally via MCP client configuration, and does not describe any authentication, authorization, or API keys. Security posture therefore depends on the MCP host environment/networking and how the MCP client/server is deployed.
Pricing
No pricing information provided; it appears to be an installable open-source Python package (MIT).
Agent Metadata
Known Gotchas
- ⚠ Cold-plate-only pressure drop: agents may incorrectly treat ΔP as full system CDU pump spec unless the user is reminded about manifold/header losses (20–50% adder).
- ⚠ B200/Gaudi 3 inputs rely on engineering estimates where vendor cold-plate geometry or R_jc is not published; results should be treated as indicative.
- ⚠ Steady-state only: agents may not model transient behavior or temperature ramps.
- ⚠ Uniform flow assumed across cold plates in rack parallel topology; real flow maldistribution may differ.
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Scores are editorial opinions as of 2026-04-04.