This MCP server enables natural language control of anaerobic digestion modeling through the internationally recognized Anaerobic Digestion Model No.
This MCP server enables natural language control of anaerobic digestion modeling through the internationally recognized Anaerobic Digestion Model No. 1 (ADM1). It bridges Claude or other LLM clients with professional wastewater treatment. The adm1 mcp mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 14 defined tools rather than through you.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Everything the assistant can do here goes through one of these:
describe_feedstock — Convert natural language feedstock description to ADM1 state variablesdescribe_kinetics — Generate both state variables AND kinetic parameters from feedstock descriptionset_flow_parameters — Configure influent flow rate and simulation timing parametersset_reactor_parameters — Set reactor-specific parameters (temperature, HRT, integration method)run_simulation_tool — Execute ADM1 simulation with current parametersget_stream_properties — Analyze detailed properties of influent, effluent, or biogas streamsget_inhibition_analysis — Process health assessment with inhibition factors and recommendationsget_biomass_yields — Calculate process performance metrics and efficiencyvalidate_feedstock_charge_balance — Verify thermodynamic consistency of feedstock definitioncheck_nutrient_balance — Analyze C:N:P ratios for process optimizationget_parameter — Retrieve current parameter values from simulation stateset_parameter — Modify specific simulation parametersYou will need 2 environment variables: MCP_TIMEOUT, GOOGLE_API_KEY. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Adm1 MCP's toolset — describe_feedstock, describe_kinetics, set_flow_parameters and 11 more — is a fair guide to whether it matches your workflow. It is maintained by puran-water; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Adm1 MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| describe_feedstock | Convert natural language feedstock description to ADM1 state variables |
| describe_kinetics | Generate both state variables AND kinetic parameters from feedstock description |
| set_flow_parameters | Configure influent flow rate and simulation timing parameters |
| set_reactor_parameters | Set reactor-specific parameters (temperature, HRT, integration method) |
| run_simulation_tool | Execute ADM1 simulation with current parameters |
| get_stream_properties | Analyze detailed properties of influent, effluent, or biogas streams |
| get_inhibition_analysis | Process health assessment with inhibition factors and recommendations |
| get_biomass_yields | Calculate process performance metrics and efficiency |
| validate_feedstock_charge_balance | Verify thermodynamic consistency of feedstock definition |
| check_nutrient_balance | Analyze C:N:P ratios for process optimization |
| get_parameter | Retrieve current parameter values from simulation state |
| set_parameter | Modify specific simulation parameters |
| generate_report | Create comprehensive professional simulation reports |
| reset_simulation | Reset all parameters to default values |
{
"mcpServers": {
"adm1-mcp": {
"command": "C:\\path\\to\\your\\venv\\Scripts\\python.exe",
"args": ["C:\\path\\to\\adm1-mcp\\server.py"],
"env": {
"MCP_TIMEOUT": "600000"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| MCP_TIMEOUT | Configuration value read at startup. | Optional |
| GOOGLE_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.