"GIS data acquisition for disaster detection and risk prediction" matching MCP servers:

Machine-readable detection lookups for SIEM enrichment and AI agents. Query 800+ LOLBAS and GTFOBins binaries plus process parent-child baselines — get risk levels, abuse categories, and MITRE ATT\&CK mappings without embedding data in prompts.

Exposes account risk status from a fraud detection pipeline, returning risk scores, flagged transaction history, and LLM-generated explanations via the get_account_risk_status tool.

MCP server for querying the Kaggle 'All Natural Disasters 1900-2021 / EOSDIS' CSV dataset via pandas, exposing search and statistics tools.

Local STDIO MCP server for object-detection annotation workflows. Manages datasets, categories, images, bounding boxes, and exports in JSONL format without modifying source images.

A Model Context Protocol server that connects LLMs to GIS operations, enabling AI assistants to perform accurate geospatial analysis including geometric operations, coordinate transformations, and spatial measurements.

MCP server exposing Interactive Brokers' portfolio risk analysis, including model greeks, implied volatility surface, what-if margin, and a local stress engine for P&L curve analysis.

Enables AI agents to assess the risk of shell commands before execution, providing a probability of harm and a suggested action (auto, warn, or confirm).

Enables detection of duplicate patient records for a Master Patient Index by scoring record pairs and providing verdicts and reasons, intended for synthetic data only.

A Windows-optimized server providing universal data analytics for JSON and CSV files through over 32 tools including schema discovery and interactive visualizations. It is specifically designed for seamless integration with Claude Desktop on Windows.

Enables multi-tier AI-detection screening on academic papers by extracting text from .tex and .docx files, splitting into standard sections, and running a pipeline of statistical and LLM-based analysis.

AI-powered fraud detection and investigation platform that exposes tools for querying, scoring, and investigating financial applications using LangGraph, MLflow, and SQLite in-memory.

AI-powered pipeline that analyzes user stories and PRDs for gaps, ambiguities, and missing acceptance criteria before coding, with MCP tools for requirement analysis and risk reporting.

Assesses code change risk with ultra-low token budget (50-100 tokens) and provides structured risk metadata for AI agents in IDEs.

Provides AI agents with quantitative risk tools such as VaR, expected shortfall, GARCH volatility, backtesting, stress testing, tail risk analysis, and credit scoring using synthetic or user-supplied data.

Enables security scanning of code projects to identify common vulnerabilities like XSS, injections, SSRF, and path traversal issues. Provides local, offline scanning with severity-grouped results and actionable fix suggestions for improving code security.

Enables AI agents to assess credit default risk, run what-if scenarios, and evaluate portfolios through natural language, backed by an explainable XGBoost model.

MCP server that exposes the credit scoring model's deterministic tools (probability of default, SHAP explanations, typicality check, financial ratios) to AI agents, enabling natural language credit risk assessment.

An MCP server that enables AI-powered fraud detection on financial transactions using rule-based and statistical analysis tools, with sample data and Gradio dashboard.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.