# System R AI API Toolkit Full Reference
# https://agents.systemr.ai

System R AI is a decision intelligence system for trading and investing.

The public product architecture has two surfaces:

1. Agentic Trading Workspace: for active traders to research, plan, journal, and review decisions.
2. API Toolkit / Developer & Finance Tools: finance tools for agents, Python workflows, notebooks, and backend services.

This agents domain serves the API Toolkit and machine-readable surfaces for developers, AI agents, MCP clients, crawlers, and developer tools.

---

## Developer Positioning

The System R API Toolkit provides finance tools for agents, Python workflows, notebooks, and backend services. It supports structured finance workflows such as risk and sizing, performance diagnostics, journal and memory workflows, market structure analysis, scenario planning, and asset or research context where supported or supplied.

## Interfaces

### MCP

MCP-compatible clients can connect to:

```
https://agents.systemr.ai/mcp/sse
```

Use the MCP server for discoverable tool access from compatible AI clients.

### REST

Current API Toolkit capabilities are described in the docs and machine readable agent metadata. Developers should use the documented MCP, SDK, REST, and OpenAPI surfaces that are live and valid.

Call a tool:

```
POST https://agents.systemr.ai/v1/tools/call
X-API-Key: sr_agent_...
Content-Type: application/json

{"tool_name": "calculate_position_size", "arguments": {"equity": "100000", "entry_price": "185.00", "stop_price": "180.00", "direction": "long"}}
```

### Python Workflows

Install the SDK package where supported:

```
pip install systemr
```

Use named methods for common operations and generic tool calls for wider tool access.

### OpenAPI

Machine-readable API reference:

```
https://agents.systemr.ai/v1/openapi.json
```

---

## Finance Tool Areas

### Risk and sizing

Position sizing, trade risk checks, pre-trade gates, options or futures sizing where inputs are supplied.

### Performance diagnostics

R-multiples, drawdown, win/loss profile, distribution, recovery, expected value, and risk-adjusted performance.

### Market structure analysis

Regime, patterns, trend structure, indicators, price structure, correlation, liquidity, volatility, and scanner logic from supplied or supported data.

### Scenario planning

What-if checks, invalidation framing, sizing boundaries, and plan structure.

### Journal and memory

Structured trade records, outcome logs, journal metrics, stored context, and memory search.

### Asset or research context

Quotes, OHLCV, news, fundamentals, scanners, or research enrichment where supported by the current provider path or supplied by the caller.

### Billing and usage

Pricing, balance, transactions, usage, metering, and key-level limits where enabled.

---

## Pricing

System R AI uses usage-based credits for paid workflows. New users may receive starter credit where offered. Current rates and billing rules should be checked on the live pricing page and through live billing endpoints.

Useful endpoints:

```
GET https://agents.systemr.ai/v1/billing/pricing
GET https://agents.systemr.ai/v1/billing/balance
GET https://agents.systemr.ai/v1/billing/transactions
GET https://agents.systemr.ai/v1/billing/usage
```

---

## Trust Boundary

System R AI is software for decision support. It is not a financial advisor, not a broker, not a signal service, and does not guarantee profits. AI outputs can be wrong. Users remain responsible for their own trading and investing decisions.

Do not present System R AI as a broker, execution venue, profit-guarantee product, or unattended automation product.