For AI agents

An agent that can call functions needs three things from a library: a list of what it may call, a schema for each call, and results it can pass on as data. RiskPY 0.4.0 provides all three, generated from the calculators' own signatures and docstrings, plus a description of the library written for the model that is about to use it.

pip install open-riskpy              # the core: sixteen tools run with no dependencies
pip install "open-riskpy[sim]"       # + NumPy: the claims-triangle tools
pip install "open-riskpy[mcp]"       # + the MCP SDK: riskpy-mcp

The tools

riskpy-tools                         # the list
riskpy-tools --json                  # the manifest: name, description, input schema, what it needs
riskpy-tools call black_scholes '{"S": 100, "K": 100, "T": 1, "r": 0.05, "sigma": 0.2}'   # 10.4506
riskpy-tools schema aggregate_loss

from riskpy import tools

tools.manifest()                                     # a list of dicts, one per tool
tools.call("basel_irb_capital", {"pd": 0.01, "lgd": 0.45, "ead": 1e6})["risk_weight"]   # 0.9232

tool	what it does
black_scholes, option_greeks, implied_vol	European options, pure Python
bond_analytics	price, Macaulay and modified duration, convexity, DV01 at a yield
basel_irb_capital, expected_loss, merton, cds_par_spread	credit risk
annuity_certain, life_annuity_due, life_insurance, net_premium_reserve	life contingencies on the AMLCR standard table
aggregate_loss	a year of claims simulated by the compiled core: Poisson frequency, lognormal severity, VaR and TVaR out
chain_ladder, mack_chain_ladder	claims triangles (need NumPy)
verify	the verification suite, as data

Every tool is a thin wrapper over a public function that the verification suite checks. Arguments are plain numbers, strings and lists: rates as decimals, times in years, probability levels as decimals. Results are JSON. The wrappers hide the conventions that bite — aggregate_loss takes a severity mean and standard deviation on the money scale and converts to the lognormal parameters itself, so an agent cannot make the commonest severity mistake there is. A tool whose extra is not installed is reported as available: false with the pip install line, and calling it raises ToolUnavailable saying the same.

The manifest's input_schema is JSON Schema, so the same list drives Anthropic tool use, OpenAI function calling, or any framework that takes schemas.

Over MCP

pip install "open-riskpy[mcp]"
riskpy-mcp --config                  # the client configuration, and the `claude mcp add` line
claude mcp add riskpy -- riskpy-mcp  # Claude Code

riskpy-mcp serves every available tool on stdio under its own name, with the schema derived from its signature and the description from its docstring, so Claude Code, Claude Desktop, Cursor and any other MCP client can price an option, size Basel capital or simulate a year of claims with numbers the verification suite stands behind.

The context pack

riskpy-context                       # Markdown: install, conventions that bite, every module's
                                     # public functions with signatures, the tools, verification
riskpy-context --json                # the same as data
riskpy-context -o AGENTS.md          # write it for the agent working in your project

It is generated from the installed package, so the signatures are the ones that will run. The "conventions that bite" section is the part to read first: rates are decimals, i is effective annual while r is continuous, LogNormal(mu, sigma) takes the underlying normal's parameters, seeds make runs reproducible, and riskpy-verify is how to check rather than trust.