Discover the Best AI Tools

DataSaudi MCP lets agents discover and query Saudi headline statistics from the public DataSaudi API in English or Arabic without a key. Most geography stops at provinces; property figures are indices, not transaction prices, and it does not cover all Saudi government datasets.

AI Economics MCP exposes calculators for token budgets, agent costs, energy estimates and verification capacity to AI clients. It calls a hosted API and returns formulas and interpretations. Outputs depend on supplied assumptions and default parameters; they are estimates, not measured bills or independently validated autonomy guarantees.

Agentic SWMM Workflow combines SWMM execution, model editing, calibration and plotting with MCP tools and auditable experiment records. Natural-language operation needs an agent/model provider. It is pre-1.0 software; published demonstrations do not validate arbitrary watershed models or replace hydrological calibration and engineering judgment.

Agentic MIKE+ exposes model inspection, editing, simulation and result plotting through MCP workers. Reading and plotting can run cross-platform without a MIKE+ license; running, editing and importing require Windows and licensed MIKE+ 2026. This experimental workflow has limited example validation and still needs engineering review.

Provides AI agents with Shopify, Amazon and Google Maps data through API and local MCP tools, including heuristic opportunity and lead scores. Hosted calls use x402 payments, Maps needs a Google key, and profitability or sales estimates are not verified business outcomes.

A local MCP bridge to Pangolinfo’s hosted product, market and search intelligence tools. Requires a Pangolinfo API key and network access; the repository contains the transport bridge, not the data engine, and commercial or legal risk outputs need independent review.

OraClaw exposes optimization, scheduling, simulation, forecasting and graph algorithms through MCP, REST and SDKs. Its hosted service limits free requests and reserves some algorithms for keyed premium access. Results depend on inputs and model assumptions; simulation or heuristic outputs are not universally optimal decisions.

rigor supplies Python CLI and MCP statistical tests, effect sizes, power calculations, regression and sequential testing with confidence intervals, references and assumption warnings. It supports batch analysis and test selection guidance. Valid conclusions depend on sampling and model assumptions; numerical results do not establish causation.

Disco searches tabular data for feature combinations and subgroup effects, tests patterns on held-out data with false-discovery control, and supplies literature references. Its hosted API needs a key and credits and receives uploaded data. Associations do not prove causation, and literature search cannot guarantee novelty.

Data Profiler MCP lets agents inspect schemas, distributions, quality issues, correlations and differences between tabular files. Large-file analysis uses capped samples and reports that limitation; results are not a complete audit of every row.

Dataloupe turns tabular files into a single offline HTML explorer with filtering, statistics, charts and SQL queries, through a CLI or MCP. The generated report embeds the data, so sharing the HTML also shares its dataset.

DecisionMatrix MCP ranks supplied options with weighted sums, weighted products and TOPSIS, and compares sensitivity to criteria weights. Its deterministic rankings depend on the supplied scores and alternatives, not an independent assessment of real-world quality.

SigRank SignalAF reads local AI session logs to derive token-processing metrics and optionally publish counts to a leaderboard. These metrics describe observed token patterns, not model quality, employee productivity or business value; publishing shares the selected counts externally.

vgi-typesafe exposes TypeSafe Choice, Noul and Score questions as DuckDB table functions through a VGI worker for row-wise classification and scoring. It requires the VGI extension, Python 3.13+ and a TypeSafe API key; data sent for inference leaves the local database.

jevframe adds pandas and Polars accessors for row-wise text classification, sentiment and scoring with Jev, returning aligned results and probabilities. It supports batching, bounded concurrency and caching. Library use requires TypeSafe credentials and sends selected row content to the service; the sponsored cached example is not general free access.

Read-only verification layer and MCP service for Malaysian public datasets, exposing freshness, reachability, licensing and observation evidence. Source status can be uncertain or discontinued; signed observations document checks rather than guaranteeing every underlying figure is correct.

MCP adapter connecting AI assistants to Autario for dataset queries, statistical analysis and chart operations. The adapter is MIT-licensed and calls the hosted Autario backend; connectors and write operations require an API key. Its code license does not make the hosted service free.

DecisionBox connects to a data warehouse, runs autonomous AI agents that write and execute SQL, and surfaces validated insights and actionable recommendations without requiring user questions.