Oraclaw

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Works with Claude Code, Codex, VS Code, Cursor, Windsurf, Claude Desktop. Read the repository before you give any tool access to your data.

npx agentmarket install connector/whatsonyourmind-oraclaw

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What this server can do and what it asks for, from its tool list and install config. Checked Oct 25, 2026.

Tools

19 read · 0 write · 0 deleteRead-only

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1 (API keys, tokens or passwords)

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Node.js package

Tools (19)

Read from the source code; the running server may differ. Descriptions are the server’s own words; hidden characters are shown as [U+…].

• analyze_decision_graph

  Graph analysis for decision networks. Computes PageRank (most influential decisions), Louvain communities (clusters of related decisions), bottlenecks (blocking nodes), and critical path. Use for dependency analysis, project planning, or understanding decision impact chains.

• analyze_graphGraph Analytics (PageRank + Louvain + Critical Path, Premium)read-only

  [Premium] Compute structural metrics of a directed weighted graph: PageRank centrality, Louvain community clusters, an optional critical path between two given nodes, and bottleneck nodes. Use to find the most influential nodes, cluster a dependency/knowledge graph, or locate chokepoints in supply or process networks. Returns per-node PageRank and community index, cluster summaries, the critical path with its weight, and bottlenecks. For a single source-to-goal route, use plan_pathfind (free). Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

• analyze_portfolio_risk

  Portfolio risk analysis computing Value-at-Risk (VaR) and Conditional VaR (Expected Shortfall) using the variance-covariance (delta-normal) method. Given portfolio weights and historical asset returns, quantifies potential losses at a given confidence level. Use for risk management, portfolio construction, regulatory capital, or stress testing.

• analyze_riskPortfolio VaR / CVaR (Premium)read-only

  [Premium] Compute portfolio Value-at-Risk and Conditional VaR (Expected Shortfall) from a historical [asset][time] return matrix and portfolio weights, accounting for cross-asset correlation. Use to size downside risk on a weighted multi-asset book, attribute risk, or run drawdown scenarios with auditable inputs. Returns VaR and CVaR (loss as a positive number) at the requested confidence, plus expected return, volatility, and the horizon used. To sample outcomes from a parametric distribution instead, use simulate_montecarlo. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no si…

• detect_anomalyAnomaly Detection (Z-score / IQR, Premium)read-only

  [Premium] Flag outlier points in a numeric series using a Z-score test (parametric, assumes near-normal) or IQR test (robust to skew/heavy tails). Use for metric monitoring, fraud/abuse signals, sensor noise, or quality control. Returns each anomaly's index, value, and score, plus the underlying statistics (mean/stdDev/threshold for Z-score; q1/q3/IQR/bounds for IQR) and an anomaly count. To project a series forward instead, use predict_forecast. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

• optimize_banditBandit Selection (UCB1 / Thompson / ε-Greedy)read-only

  Select the next option to try from 2+ variants that each have observed pull/reward history, balancing exploitation against exploration (UCB1, Thompson sampling, or epsilon-greedy). Use when you must pick one arm now from A/B test variants, ad/email/copy options, or ranked recommendations and have past trial counts. Returns the chosen arm plus exploitation score, exploration bonus, and a regret estimate. For per-call context features use optimize_contextual; for continuous parameters use optimize_cmaes.

• optimize_cmaesCMA-ES Continuous Optimization (Premium)read-only

  [Premium] Optimize N continuous parameters against a weighted-sum objective using CMA-ES, suited to non-convex/noisy/gradient-free landscapes. Use for hyperparameter search, simulator calibration, or control-policy tuning where you supply per-dimension objective weights. Returns the best parameter vector, its objective value, iteration/evaluation counts, and a converged flag; stochastic init means repeated runs may differ. Use optimize_evolve for discrete spaces and solve_constraints for linear/MIP constraints. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

• optimize_contextualContextual Bandit (LinUCB)read-only

  Select the best option given a numeric context/feature vector, using a LinUCB contextual bandit that learns per-context preferences from optional history. Use when the best choice changes with situational features that vary call-to-call (user/segment attributes, time of day, current regime). Returns the chosen arm with its LinUCB expected reward and confidence width. If you have no per-call features, use optimize_bandit.

• optimize_evolveGenetic Algorithm (discrete + multi-objective)read-only

  Run a genetic algorithm over a fixed-length gene vector (binary, integer, real, or permutation bounds) against a weighted-sum fitness, with an optional Pareto frontier for multi-objective runs. Use for discrete or mixed search spaces (feature selection, integer allocation, permutation/TSP-style problems) or when you want several non-dominated solutions. Returns the best chromosome and fitness, the Pareto frontier when applicable, the convergence generation, total generations, and recent fitness history; results vary run to run (stochastic). For smooth continuous objectives, use optimize_cmaes.

• plan_pathfindA* / Yen's K-Shortest Pathsread-only

  Find the shortest path (or k-shortest paths) between a start and end node in a weighted directed graph using A* with selectable heuristic (zero=Dijkstra, time, cost, risk, weighted) and Yen's algorithm for alternatives. Use for routing, dependency resolution, or 'how do I get from X to Y' over a graph; set kPaths>1 for alternatives. Returns the path node IDs, total cost, a time/cost/risk breakdown, nodes explored, and a found flag. For centrality/communities use analyze_graph; for task-to-slot assignment use solve_schedule.

• predict_bayesianBayesian Belief Update (Beta posterior)read-only

  Update a prior probability with weighted evidence signals using a Beta posterior (the prior seeds Beta(prior*10, (1-prior)*10)). Use for incremental belief revision: start from a baseline probability and fold in signals, each a value in [0,1] with a weight, to get a revised posterior. Returns the updated posterior, the prior, per-factor contributions, posterior mean and variance, and a sharpness/calibration score. To combine N independent point predictions use predict_ensemble; to sample a full distribution use simulate_montecarlo.

• predict_ensembleEnsemble Multi-Model Consensusread-only

  Combine 2+ model point predictions into one consensus using weighted voting, stacking, or Bayesian model averaging, weighting each model by its confidence or supplied historicalAccuracy. Use to fuse heterogeneous predictors (statistical, ML, and human forecasters) into a single number with an uncertainty estimate. Returns the consensus value and confidence, per-model weight share, Shannon entropy of the weights, a cross-model agreement score, epistemic/aleatoric/total uncertainty with a confidence interval, and per-model contributions. To score agreement on a single event probability instead, …

• predict_forecastTime Series Forecast (ARIMA / Holt-Winters, Premium)read-only

  [Premium] Forecast the next N values of one evenly-spaced numeric time series using ARIMA (non-seasonal trend) or Holt-Winters (additive seasonal, set seasonLength). Use for short-to-medium horizon point forecasts of demand, KPIs, or capacity. Returns the point forecast array plus lower/upper confidence bands and the fitted model description. ARIMA requires at least 20 observations; Holt-Winters needs at least 2 x seasonLength. To flag outliers instead of projecting, use detect_anomaly. Premium: needs an ORACLAW_API_KEY OR a per-call x402 payment (no signup).

• score_calibrationCalibration Scoring (Brier + Log)read-only

  Measure how well a set of probability predictions matched observed binary outcomes, returning the Brier score and log score (lower is better). Use to evaluate a forecaster's or model's calibration: predictions[i] is the probability assigned to event i and outcomes[i] is 1 if it occurred, else 0 (arrays must be equal length). Returns brier_score, log_score, the number of predictions, and the mean predicted vs mean observed rate. To measure agreement across multiple sources instead, use score_convergence.

• score_convergenceMulti-Source Probability Convergenceread-only

  Score how strongly multiple independent sources agree on a single event's probability, using Hellinger-distance agreement plus penalties for dispersion/uncertainty and a freshness weight (recency, source volume, and confidence). Use to fuse 0..1 estimates from polls, prediction markets, or model outputs into one number. Returns a 0..1 convergence score, the volume-weighted consensus probability, source count, and component breakdown. To combine N point predictions instead, use predict_ensemble.

Signals

Stars

13

Forks

2

Open issues

0

Weekly downloads

35

Last commit (this folder)

Jun 23, 2026 (4 months ago)

Last release

Jun 23, 2026 (4 months ago)

Last package publish

Jun 23, 2026 (4 months ago)

From GitHub (fetched Oct 24, 2026) and the package registry.

Details

Registry name

io.github.Whatsonyourmind/oraclaw

Version

1.5.1

Auth

API key

Packages

npm @oraclaw/mcp-server

License

MIT

Repository

github.com/Whatsonyourmind/oraclaw

First seen

Oct 24, 2026

Last checked

Oct 24, 2026

Source

Official MCP Registrygithub-graphql