Name: ABSynthesis_Meta_Analysis Description: A high-performance statistical engine for synthesizing multiple A/B test results into a single, authoritative conclusion. This tool uses Random Effects Meta-Analysis to aggregate disparate experiments, providing a mathematically rigorous alternative to...
Risk signalsAccepts raw HTML/template content (payload)
Part of the Scientific Microservices server.
Free to start. No card required.
AI agents invoke absynthesis to trigger processes or run actions in Scientific Microservices. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
absynthesis can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"absynthesis": {
"limits": [
{
"counter": "absynthesis_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Scientific Microservices policy for all 5 tools.
These attack patterns abuse exactly the kind of access absynthesis gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Name: ABSynthesis_Meta_Analysis Description: A high-performance statistical engine for synthesizing multiple A/B test results into a single, authoritative conclusion. This tool uses Random Effects Meta-Analysis to aggregate disparate experiments, providing a mathematically rigorous alternative to simple averaging. Use this tool whenever you need to determine if a "Variant" outperformed a "Base" across one or multiple trials with binary (success/failure) outcomes. Why Use This Tool? Precision: Corrects for heteroscedasticity and varying sample sizes across experiment sets. Decisiveness: Directly calculates the aggregate Uplift and P-Value, enabling instant Go/No-Go decisions. Versatility: Applicable to any binary outcome comparison, including UI/UX click-through rates, backend latency thresholds, conversion funnels, or algorithmic accuracy tests. Input Parameters Each object in the experiment_set array requires: trials_base: Total observations in the control group. successes_base: Number of successful outcomes in the control group. trials_variant: Total observations in the experimental group. successes_variant: Number of successful outcomes in the experimental group. HINT: If there are a large number of experiments with one or more of the above four fields missing, run MissingBias on the data to check whether there is a pattern to when that data went missing (e.g. Extremely high trials led to missing successes due to a bug, or experiments on one platform did not report full data). Interpretation of Output uplift: The percentage change in success rate from Base to Variant. Positive (>0): Variant is performing better. Negative (<0): Base is performing better; reject the Variant. p_value: Statistical significance (2-tailed). ≤0.05: The result is statistically significant. Trust the uplift value if it is positive. >0.05: The result is noise. Do not act on the uplift value, regardless of how large it appears. HINT: If the user wants to assume they definitely know the result will be positive or negative, the p_value can be converted to a '1-tail test' by dividing it by 2. Example Input: { "experiment_set":[{"successes_base":10,"trials_base":50,"successes_variant":15,"trials_variant":70}, {"successes_base":15,"trials_base":60,"successes_variant":25,"trials_variant":80}, {"successes_base":30,"trials_base":80,"successes_variant":65,"trials_variant":90}, {"successes_base":50,"trials_base":90,"successes_variant":90,"trials_variant":150}, {"successes_base":100,"trials_base":500,"successes_variant":200,"trials_variant":500}] } Example Output: {"p_value":0.0063, "uplift":0.3779}. It is categorised as a Execute tool in the Scientific Microservices MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Scientific Microservices MCP server in PolicyLayer and add a rule for absynthesis: 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 Scientific Microservices. Nothing to install.
absynthesis 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 absynthesis 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 absynthesis. 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.
absynthesis is provided by the Scientific Microservices MCP server (https://mcp.scientificmicroservices.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Scientific Microservices tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.