{"id":"thermal-mcp-server","name":"thermal-mcp-server","homepage":"https://pypi.org/project/thermal-mcp-server/","repo_url":"https://github.com/riccardovietri/thermal-mcp-server","category":"ai-ml","subcategories":[],"tags":["mcp","python","thermal-engineering","liquid-cooling","gpu","datacenter","physics-modeling"],"what_it_does":"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.","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"],"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.","alternatives":["Custom internal thermal/hydraulic models in engineering code (e.g., MATLAB/Python notebooks)","Spreadsheet/ETL-based thermal resistance calculators used in procurement workflows","Vendor-provided thermal/cooling calculators and datasheets (where available)","General-purpose CFD tools (higher fidelity but heavier) for validation of critical cases"],"af_score":58.5,"security_score":31.5,"reliability_score":28.8,"package_type":"mcp_server","discovery_source":["pypi"],"priority":"low","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-04-04T21:42:11.952918+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":true,"mcp_server_url":null,"has_sdk":false,"sdk_languages":[],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["None mentioned (local MCP server launched by host process)"],"oauth":false,"scopes":false,"notes":"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":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing information provided; it appears to be an installable open-source Python package (MIT)."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":58.5,"security_score":31.5,"reliability_score":28.8,"mcp_server_quality":70.0,"documentation_accuracy":75.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":100.0,"rate_limit_clarity":0.0,"tls_enforcement":20.0,"auth_strength":20.0,"scope_granularity":0.0,"dependency_hygiene":70.0,"secret_handling":60.0,"security_notes":"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.","uptime_documented":0.0,"version_stability":35.0,"breaking_changes_history":30.0,"error_recovery":50.0,"idempotency_support":"true","idempotency_notes":"Physics/tool calls are pure computations given inputs; repeated calls should be idempotent. No state changes are described.","pagination_style":"none","retry_guidance_documented":false,"known_agent_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."]}}