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Simulate wastewater treatment processes in natural language for AI-driven analysis and automation.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "QSDsan Engine MCP" yet — see the docs or source repo.
Simulate a wastewater treatment process with these parameters: influent COD 450 mg/L, ammonia nitrogen 35 mg/L, daily flow 12,000 tons. Output the main treatment stages, key operating parameters, and expected effluent indicators.
A structured process description with treatment stages, key parameters, and expected effluent results.
Compare two wastewater treatment operating scenarios: Scenario A increases aeration intensity, and Scenario B extends sludge age. Explain their impact on COD removal, energy consumption, and effluent stability.
A comparative analysis of the two scenarios to help choose a more suitable operating strategy.
Explain how to call QSDsan Engine via MCP or CLI to run batch simulations on multiple wastewater treatment parameter sets and summarize the results in a table.
Integration-ready guidance with invocation steps and a summarized output format for automation workflows.
Monitor industrial process data and get anomaly analysis with actionable recommendations.
Simulate dynamic actions for any query with persistent state across sessions.
Give AI agents spa-like resets between tool calls to improve pacing.
Control FEFLOW 7.5 via MCP for groundwater modeling and result export.
Build, debug, and manage software tasks with natural language across LLMs.
Generate MCP tools from API specs and query them in natural language.