io.github.psyb0t/predictalot (MCP)

Self-hosted MCP server for time-series forecasting and tabular ML via foundation models. It is presented as an HTTP-based service and targets workflows involving machine-learning and forecasting use cases, including zero-shot approaches.

🛠️ Key Features

Time-series forecasting
Tabular machine learning (tabular-ml)
Foundation-model-based workflows (foundation-models)
Includes support for model/tooling topics such as chronos, timesfm, moirai, and sundial
Environment and stack references include docker, fastapi, python, pytorch, cuda, and scikit-learn
Mentions gradient-boosting tooling topics: lightgbm and xgboost

🚀 Use Cases

Forecasting time-series data (time-series-forecasting, forecasting)
Zero-shot forecasting scenarios (zero-shot-forecasting, zero-shot)
Tabular ML on structured datasets (tabular-ml, machine-learning)

⚡ Developer Benefits

MCP compatibility for integrating foundation-model forecasting into developer tooling (mcp)
Deployable via Docker (docker)
Python-focused stack (python, pytorch, fastapi)

⚠️ Limitations

No specific MCP tool list, capabilities, or toolCount details were provided in the available source data.

predictalot

One HTTP service, two model families, zero ceremony.

Foundation time-series — 5 zero-shot forecasters (chronos-2, timesfm-2.5, moirai-2, toto-1, sundial-base-128m). Hand them a context window, get quantile or sample-path forecasts. No training step. Six modality-specific endpoints under /v1/timeseries/<type>/.
Tabular ML — 9 supervised learners (lightgbm, xgboost, hist-gbt, random-forest, logistic, mlp, svm-rbf, knn, naive-bayes) + 3 meta-learners (calibrated, stacking, diversified). Train on YOUR engineered features, persist server-side by modelId, forecast on the latest snapshot. Under /v1/tabular/.
MCP — streamable-HTTP tools at /mcp. One named tool per (FM type, model) cell plus per-type ensemble + listing. Tabular endpoints are HTTP-only for now.

Quick start

bash

docker run -d --name predictalot \
  -v $HOME/predictalot-models:/models \
  -e PREDICTALOT_AUTH_TOKENS=changeme \
  -p 8080:8080 \
  psyb0t/predictalot:latest

# Zero-shot FM forecast
curl -s http://localhost:8080/v1/timeseries/univariate/forecast \
  -H "Authorization: Bearer changeme" -H "Content-Type: application/json" \
  -d '{"model":"chronos-2","context":[[10,11,12,13,14,15,16,17,18,19,20]],"config":{"horizon":5}}' | jq

# Train + persist a tabular model on your own features
curl -s http://localhost:8080/v1/tabular/train \
  -H "Authorization: Bearer changeme" -H "Content-Type: application/json" \
  -d '{"modelId":"my-model","backend":"lightgbm","target":[[100,101,99,...]],
       "features":[{"rsi":[55,58,...],"macd":[0.3,0.4,...]}],
       "config":{"mode":"direction","horizon":3,"nEstimators":400}}' | jq

# Then forecast on the latest snapshot
curl -s http://localhost:8080/v1/tabular/forecast \
  -H "Authorization: Bearer changeme" -H "Content-Type: application/json" \
  -d '{"modelId":"my-model","features":[{"rsi":[58],"macd":[0.4]}]}' | jq

Documentation

Doc	What it covers
docs/timeseries.md	Foundation time-series API. All 5 models (capabilities + per-model quirks + what each is recommended for), all 6 forecast types, per-type ensemble with `weights` + `memberOverrides`, `extra` per-call hatch, `/models` listings.
docs/tabular.md	Tabular ML API. All 9 backends (what each is recommended for), 3 modes (direction / value / quantile), tier-1/2/3 config knobs, the 3 meta-learners (calibrated / stacking / diversified), storage layout.
docs/mcp.md	MCP streamable-HTTP server: tool naming, args, current scope (FM only).
docs/configuration.md	Every `PREDICTALOT_*` env var.
docs/architecture.md	Multi-venv sidecar pattern for sundial, CPU vs CUDA images, multi-stage build.
docs/accuracy.md	Benchmark sMAPE + latency on academic + real-world datasets. Honest takeaways including which models lose.
docs/errors.md	Error contract: 400 / 401 / 404 / 413 / 422 / 503 shapes.

CHANGELOG.md tracks per-version changes.

License

Code: WTFPL (see LICENSE). The MCP plugin under .agents/plugins/predictalot/ is MIT (its own LICENSE). Foundation models retain their upstream licenses — chronos-2 / timesfm-2.5 / toto-1 / sundial-base-128m: Apache 2.0; moirai-2: CC-BY-NC-4.0 (non-commercial). Tabular backends use their upstream licenses — lightgbm / xgboost / scikit-learn: permissive. Review each before commercial use.

Predictalot