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entropy

entropy

Remote
github.com/terradev-cloudAI & Agents0Updated 5w ago
Repository

About entropy

entropy is an MCP server that provides agents with OS-based cryptographic randomness and verifiable commit-reveal records. It offers sampling, statistical tests, and stateless bandit selection, with third-party verification of draws.

Key features

  1. 01CSPRNG-based random draws
  2. 02Sampling, shuffling, and selection
  3. 03Commit-reveal draw verification
  4. 04Statistical test battery
  5. 05Stateless bandit selection
  6. 06Seeded reproducible results

Use cases

  1. 01Choosing fairly among options
  2. 02Verifying an agent's random draw
  3. 03Testing sequences for statistical patterns
  4. 04Reproducing a seeded draw

MCP support

The MCP server exposes four tools: random, commitment, test, and explore. Clients can request draws, create and verify commit-reveal records, run statistical tests, and perform bandit selection.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://entropy-mcp.terradev.cloud/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

4
ParameterTypeRequiredDescription
seedstringNooptional seed for a reproducible draw
paramsobjectNooperation-specific arguments
operationenumYesbytes | int | shuffle | choose | sample | constrained

Details

From the project README

entropy-mcp

Entropy gives agents randomness they cannot produce themselves, and proof they did not manipulate it.

The evidence

Language models cannot generate statistically sound randomness. This is measured, not speculative:

  • arXiv:2601.05414 — 11 frontier models, 15 distributions: 7% median pass rate in batch mode; 10 of 11 models passed zero distributions in stateless mode.
  • arXiv:2604.06543 — the knowing-doing gap: models can describe correct sampling and still fail to do it. Asking a model to generate its own seed reproduces the same bias.
  • arXiv:2606.06622 — UnpredictaBench: models are measurably predictable where they should be random.

The stateless point

Agents operate in exactly the regime where models fail hardest: independent, stateless calls with no memory of prior draws. Every "pick a random number" an agent answers from its own weights is a draw from a biased, predictable source. Entropy moves the draw out of the model and into the OS CSPRNG — and the commit-reveal chain proves the draw that happened is the draw that was reported.

Install

pip install entropy-mcp          # stdio core + stamp-mcp dependency
pip install entropy-mcp[http]    # adds the aiohttp HTTP transport

Quickstart

{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"commitment","arguments":{
  "operation":"draw",
  "draw_spec":{"operation":"shuffle","params":{"items":["alice","bob","carol","dave"]}},
  "description":"who goes first"}}}

One call returns the shuffled list plus a commitment record, a reveal record, and a verification block — the full chain a third party can check with commitment operation:"verify".

Command reference

Four tools. operation selects the primitive; params carries its arguments; seed makes any draw reproducible.

ToolOperations
randombytes (CSPRNG, hex/base64) · int (unbiased [min,max] via rejection sampling) · shuffle (Fisher-Yates + permutation) · choose (weighted/unweighted, with/without replacement) · sample (uniform, normal, lognormal, exponential, triangular, beta) · constrained (independent per-attribute sampling + chi² conformance proof)
commitmentcommit (hash seed, bind draw spec, attest) · reveal (verify seed, re-execute, attest outcome) · draw (commit + execute + reveal in one call) · verify (pure third-party verification, no local state) · list (local commitment log)
testStatistical battery: frequency, chi², runs, longest run, serial, Shannon, KS, gap
exploreBandit selection: epsilon_greedy, thompson, ucb1 (stateless)

Every seeded command is deterministic given its inputs and seed — byte-identical across processes, machines, and Python versions. Only the raw OS-entropy paths are non-deterministic, and they say so.

How commit-reveal works

  1. commit generates (or accepts) a 32-byte seed, stores only sha256(seed), binds it to your draw spec via canonical hash, timestamps it via Stamp's NTP, optionally anchors it to a drand round, and attests the whole record. The seed goes to you. It is never stored server-side.
  2. reveal takes the seed back, verifies it hashes to the committed seed_hash, re-executes the draw deterministically, and attests the outcome.
  3. verify_chain checks, independently of any server state:
CheckProves
seed_produces_commitment_hashthe revealed seed is the one committed to
commitment_attestation_validthe commitment record is intact and attested
reveal_attestation_validthe reveal record is intact and attested
commitment_precedes_revealthe commit happened before the reveal
outcome_reproduciblethe reported outcome is what the seed actually produces
params_hash_matchesthe draw spec wasn't altered after commitment
beacon_anchor_validthe commitment predates the drand round's publication (when anchored)

What this is not

  • Not a statistics package — the summary block and test battery are conveniences, not an analysis suite.
  • Not a simulation engine — no Monte Carlo, no sensitivity analysis, no fitting.
  • Not a randomness beacon — drand anchoring is optional hardening, not a source.
  • Not NIST-certified — the test battery is informed by SP 800-22, in the spirit of it.

Configuration

Env varDefaultPurpose
ENTROPY_COMMITMENT_LOG~/.entropy/commitments.jsonlcommitment log path (0600, capped at 10k records)
ENTROPY_STAMP_URLhttps://stamp-mcp.terradev.cloudremote Stamp endpoint (local stamp_mcp import preferred)
ENTROPY_BEACON_URLhttps://api.drand.shdrand API base
ENTROPY_HOST / ENTROPY_PORT127.0.0.1 / 8001HTTP transport bind

Threat model

See THREAT_MODEL.md. The honest summary: Entropy proves a draw was not manipulated; it cannot prove a draw was not discarded.

License

Apache 2.0.

Comments

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