Low Risk

stats_distribution-fit

Fit data to common distributions and rank by goodness of fit. Use when fitting data to standard distributions (normal, lognormal, uniform). Provide a data array. Returns: best-fit distribution, parameters (mean, std, etc.), goodness-of-fit statistics (KS test, chi-squared), and Q-Q plot data.

Part of the Quantoracle MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.

AI agents call stats_distribution-fit to retrieve information from Quantoracle without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though stats_distribution-fit only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

quantoracle-quantoracle.yaml
tools:
  stats_distribution-fit:
    rules:
      - action: allow

See the full Quantoracle policy for all 74 tools.

Tool Name stats_distribution-fit
Category Read
Risk Level Low

View all 74 tools →

Agents calling read-class tools like stats_distribution-fit have been implicated in these attack patterns. Read the full case and prevention policy for each:

Browse the full MCP Attack Database →

Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.

What does the stats_distribution-fit tool do? +

Fit data to common distributions and rank by goodness of fit. Use when fitting data to standard distributions (normal, lognormal, uniform). Provide a data array. Returns: best-fit distribution, parameters (mean, std, etc.), goodness-of-fit statistics (KS test, chi-squared), and Q-Q plot data.. It is categorised as a Read tool in the Quantoracle MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on stats_distribution-fit? +

Add a rule in your Intercept YAML policy under the tools section for stats_distribution-fit. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Quantoracle MCP server.

What risk level is stats_distribution-fit? +

stats_distribution-fit is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit stats_distribution-fit? +

Yes. Add a rate_limit block to the stats_distribution-fit rule in your Intercept 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.

How do I block stats_distribution-fit completely? +

Set action: deny in the Intercept policy for stats_distribution-fit. 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.

What MCP server provides stats_distribution-fit? +

stats_distribution-fit is provided by the Quantoracle MCP server (QuantOracle/quantoracle). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policies on Quantoracle

Open source. One binary. Zero dependencies.

npx -y @policylayer/intercept
github.com/policylayer/intercept →
// GET IN TOUCH

Have a question or want to learn more? Send us a message.

Message sent.

We'll get back to you soon.