What does a real call return?
With n = 10 and a reported mean of 3.48, the implied sum is 34.8, which is not an integer. The nearest reachable mean is 3.50 and the verdict is inconsistant.
GRIMMER example: n = 5, reported mean 3.00, observations from 1 to 5. A reported standard deviation of 0.80 returned inconsistant: no integer dataset reaches it, and the nearest reachable value is 0.71. A reported 1.00 returned consistant; for example 2, 2, 3, 4, 4 has mean 3 and sample standard deviation 1. Both calls were recorded through the public MCP endpoint on 6 November 2026 (inputs and responses).
Recorded on 2 November 2026 through the public MCP endpoint, service release 0.17.1. Inputs and full responses, response SHA-256 182bc60b036a…. Verdict values are the literal values the tool returns. Run the same inputs yourself to compare; elapsed times vary, and a new release may change the figures.
Checked against
- Integer mean-grid cases, sum-of-squares bounds, rounding-window enumeration, impossible zero standard deviation and sample-SD constraints.
Recorded on release 0.16.1. These checks cover the listed cases, not every input. Evidence cards.
How it works
GRIM (Granularity-Related Inconsistency of Means) tests whether an integer sum can produce a reported rounded mean at a given sample size. The standard-deviation screening also considers the possible sums compatible with rounding and necessary integer second-moment conditions.
Inputs and units
Provide sample size, reported mean, decimal precision, optional standard deviation and the minimum and maximum integer response values.
What the result contains
The result reports inconsistencies, nearest reachable quantities and the assumptions used. The verdict value "consistant" (the literal value the tool returns, in French) means that the implemented filters found no contradiction; it does not reconstruct or verify the original observations.
Example MCP call
{
"name": "audit_statistics",
"arguments": {
"n": 10,
"mean": 3.48,
"decimals": 2,
"min_value": 1,
"max_value": 5
}
}Send this tool name and arguments through a connected MCP client. Discover the authoritative input schema with tools/list.
Call this engine in one command
Node.js 20+ and npm. This downloads a small remote MCP client; it does not install the engine locally.
npx --yes --package https://scorecompute.com/downloads/scorecompute-call-0.14.2.tgz scorecompute-call audit_statistics --arguments '{"n":10,"mean":3.48,"decimals":2,"min_value":1,"max_value":5}'Client download, PowerShell input and verification →
Cite this engine
Engine identified by source hash, source SHA-256 23d2302b1caf…. Evidence recorded on release 0.16.1; the public service currently runs 0.17.1. Replace the access date when you cite it.
ScoreCompute. GRIM and GRIMMER statistics checks (audit_statistics), identified by source hash, source SHA-256 23d2302b1cafa26b97ea604221fe2ac0f9727e32c3701842c9c2964a321f78f2. Evidence release 0.16.1, service release 0.17.1. https://scorecompute.com/engines/audit-statistics/ (accessed YYYY-MM-DD).
All engine citations · Evidence card
Execution and availability
ScoreCompute exposes this tool through MCP Streamable HTTP. Rust computation runs on a connected worker; the public website and MCP gateway run separately. Among these 22 scientific tools, CUDA is implemented for simulate_pi; the other scientific engines run on CPU. The separate contributor network runs two compiled public workloads: a π estimate and fixed synthetic schedule probes. Requests are bounded and concurrent work may be refused when capacity is occupied.
Record inputs, assumptions and returned provenance when sharing a result. The public observatory displays software client names and tool activity, without publishing calculation arguments or results.