hypothesis_testing
Run statistical hypothesis tests (t-test, chi-square, ANOVA, normality, Mann-Whitney) with p-values and effect sizes.
This record as markdown: /tools/io-github-iowarp-adios-mcp/hypothesis-testing.md
What hypothesis_testing does on CLIO Adios
AI agents invoke hypothesis_testing to trigger actions in CLIO Adios. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why hypothesis_testing is rated High
This tool executes statistical analysis code/algorithms on provided data. While it does not modify the underlying data (ruling out Write/Destructive) and operates within a scientific analysis context, it triggers computational operations whose outputs and side effects depend on the input arguments—characteristic of Execute category.
From the tool's definition Tool description states it 'Run[s] statistical hypothesis tests' with multiple test types (t-test, chi-square, ANOVA, normality, Mann-Whitney) and computes statistical outputs (p-values, effect sizes).
Attacks that exploit this kind of access
The rule that runs hypothesis_testing safely
PolicyLayer is an MCP gateway: it sits between your AI agents and CLIO Adios, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For hypothesis_testing, this is the rule to start with:
hypothesis_testing stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect CLIO Adios, apply this rule, and every hypothesis_testing call is checked against it from then on.
Questions about hypothesis_testing
Run statistical hypothesis tests (t-test, chi-square, ANOVA, normality, Mann-Whitney) with p-values and effect sizes. It is categorised as a Execute tool in the CLIO Adios MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the CLIO Adios MCP server in PolicyLayer and add a rule for hypothesis_testing: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches CLIO Adios. Nothing to install.
hypothesis_testing is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the hypothesis_testing rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for hypothesis_testing. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
hypothesis_testing is provided by the CLIO Adios MCP server (pypi:clio-kit). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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