soif-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| estimate_waterA | Estimate the freshwater consumed (mL) by one LLM call. Provide real token counts when known; otherwise pass |
| estimate_from_usageA | Estimate water (mL) from a real API usage object — the accurate path.
|
| compare_modelsB | Rank candidate models by mid-scenario water use for a workload. Returns models sorted least- to most-thirsty, each with its tier and
water range in mL. |
| pick_low_water_modelA | Pick the least-thirsty candidate model that meets a capability floor. Use in agent graphs to route each step: set |
| list_known_modelsA | List models soif recognises, with their size tier and default hosting. Unknown models still work everywhere (they fall back to the "large" tier with an explicit assumption), but known models get calibrated tier/provider/region defaults. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| methodology | How soif estimates water, in brief, with factor tables. |
TDQS
Scored across 5 tools
Most tools have clearly distinct purposes: estimate_from_usage handles API usage objects, estimate_water handles manual/approximate inputs, compare_models ranks, and pick_low_water_model selects. The only mild ambiguity is between compare_models and pick_low_water_model, which both operate on candidate models and min_tier, though one returns a ranked list and the other returns a single pick.
All tool names follow a descriptive snake_case verb-first pattern: estimate_*, compare_*, pick_*, list_*. The only slight inconsistency is estimate_from_usage using a prepositional phrase rather than the simple verb_noun shape used by the others.
Five tools is well-scoped for this domain. Each tool earns its place: estimation from usage objects, estimation from manual inputs, model comparison, low-water model selection, and known-model metadata.
The toolkit covers the full workflow: lookup known models, estimate water for a single call via either real usage or approximate inputs, compare models for a workload, and pick the least-thirsty model for routing. There are no obvious dead ends or missing core operations for this focused purpose.