Predictalot MCP server
Self-hosted MCP server for time-series forecasting and tabular ML via foundation models.
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2 stars
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Predictalot tools
No tool declarations could be read from the package source. They show once the server is installed.
How Predictalot compares
Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 80/100
- higher than 60% of 48,255 servers
- GitHub stars
- 2
- more than 75% of 28,688 servers
Release history: 3 versions in the registry since 2026-08-01, latest 2026-10-10, typically one every 54 days.
Install Predictalot in Claude Code, Cursor or VS Code
claude mcp add predictalot -- docker run -i --rm docker.io/psyb0t/predictalot:v1.2.2
What the publisher says
From the Predictalot repository's README, as published. We do not edit it. Read it on GitHub
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//.
- 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
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]}]}' | jqDocumentation
CHANGELOG.md tracks per-version changes.
Agent integrations
The skill works in any agent that reads .agents/skills/, and installs natively in the clients below.
Claude Code
claude plugin marketplace add psyb0t/agents
claude plugin install predictalot@psyb0tShortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Predictalot: common questions
- Is Predictalot MCP server safe?
- Mostly: it is graded B (80/100). Read the Predictalot safety report
- How do I install Predictalot?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does Predictalot need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is Predictalot maintained?
- The last commit was 95 days ago (2026-08-01). The latest release is v1.2.2.
- How does Predictalot compare with other MCP servers?
- safety score 80/100 (higher than 60% of 48,255 servers); github stars 2 (more than 75% of 28,688 servers). Full comparison.