forex-predict-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": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_market_featuresA | Fetch and compute the feature vector for a forex pair. ticker: 'EURUSD=X' or 'GBPUSD=X' Returns {ticker, as_of, features: {feature_name: value}} on success, or {error: reason} if the ticker is unsupported or data cannot be fetched. Call this first, inspect the features, then pass them to get_forex_signal. |
| get_forex_signalA | Run the XGBoost model with pre-computed features from get_market_features. ticker: 'EURUSD=X' or 'GBPUSD=X' features: the features dict returned by get_market_features() Returns {ticker, as_of, signal (UP/DOWN), prob_up, confidence} on success, or {error: reason} if features are missing or the ticker is unsupported. |
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 have clearly distinct roles: one fetches and computes market features, the other consumes those features to produce a signal. There is no ambiguity about which tool to call for a given step.
Both tools follow the same verb_noun pattern (get_market_features, get_forex_signal), making the naming predictable and consistent.
With only two tools, the set is minimal but appropriate for a focused prediction pipeline. It feels thin for a broader domain, but for the stated purpose of feature extraction plus signal generation, it is acceptable.
The tools cover the core workflow: fetch features, then run the model. There is no missing operation for the primary use case, though additional tools like backtesting or historical data retrieval would enhance completeness.