FAIM MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | Set to 'development' for verbose logging | production |
| FAIM_API_KEY | Yes | Your FAIM API key |
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| list_modelsA | List all available forecasting models and their capabilities. Returns information about Chronos2, TiRex, and other available models, including supported output types and features. |
| forecastA | Perform time series forecasting using FAIM platform. Supports both point forecasting (single value) and probabilistic forecasting (confidence intervals). Can handle univariate and multivariate time series data. Currently supported models: Chronos2 (default, recommended for multivariate) and TiRex (fast, univariate only). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools serve entirely different purposes: one performs forecasting while the other lists available models. There is no ambiguity or overlap.
Both tools follow the descriptive verb_noun pattern (forecast, list_models) and use consistent lowercase_with_underscores naming.
With only two tools, the server feels underdeveloped for a forecasting platform. Expected tools like data management, evaluation, or model configuration are missing.
The tool surface is severely incomplete. Typical forecasting workflows require data upload, model training, evaluation, and result retrieval, none of which are present beyond the basic forecast and model listing.