8 #risk-management MCP Servers & Clients

AI-native risk intelligence for trading agents

System-R-AI

Python SDK for the System R AI API Toolkit.

Onboard MCP

viaonboard

AI-native customer onboarding: live projects, KPIs, tasks, and emails with Onboard MCP. Connect Onboard to Claude or any MCP-compatible AI client to seamlessly surface blocked tasks, draft customer emails, monitor KPIs, and take action—right from your AI workspace. Access 27+ pur

Agent-to-agent knowledge exchange for trading intelligence. Agents publish empirical findings (sector failures, entry signals, exit patterns, regime signals), vote based on their own results, and earn reputation. Quality

Finance Toolkit

JerBouma

The Finance Toolkit gives AI assistants access to 200+ financial metrics, all calculated

Riskmodels

BlueWaterCorp

RiskModels provides institutional-grade equity risk decomposition via MCP. Analyze any US stock or portfolio through L1/L2/L3 factor attribution — market, sector, subsector, and residual components — with executable ETF hedge ratios for precise risk management. Built for portfoli

Finturb Investment Signals

ai-intellicore

We design and operate proprietary signal systems that turn noisy market, macro, and media data into structured, calibrated intelligence — exposed through web dashboards, a Signals API, and a public MCP server any AI assistant can reason over directly. The eight pillars

CPZAI

CPZ-Lab

CPZAI MCP server. Build, backtest, and deploy quantitative trading strategies from your AI agent. 29 tools: list_strategies, get_strategy, create_strategy, update_strategy, get_backtest_results, list_orders, place_order, list_positions, sync_portfolio, plus dev-time tools. Multi-

Ask n0brains first. Your agent submits a planned trade (asset, side, entry, stop, leverage) and gets back a graded A–F conditions assessment from live market data — positioning crowding, event risk inside the horizon, liquidation clusters, market regime. 28 tools including win-ra