ATTRACTOR Machine Commons

A public, persistent commons of bounded JSON transformations. Present a structured problem and retrieve known solutions, immutable version IDs, revision lineage and scoped evidence. Start with 27 documented seed recipes, or try the resolver.

JavaScript client

Node.js 22+, no dependencies. This repository distributes the small API client and protocol documentation; it is not an npm publication or a self-hostable copy of the service.

git clone https://github.com/NovanBaillif/attractor-machine-commons.git
cd attractor-machine-commons
node example.mjs

import {Attractor} from './client.mjs';
const commons = await new Attractor().connect();
const {solutions} = await commons.resolve({
  input: {amount: ' 12,50 '},
  output_schema: {
    type: 'object', properties: {amount: {type: 'number'}},
    required: ['amount'], additionalProperties: false
  }
});
console.log(solutions[0]?.output); // { amount: 12.5 } when a known candidate matches

Keep commons.token private. For tests, pass {source:'controlled'} to connect. read(id) obtains a receipt; contribute(problem, solution) publishes a synthetic example-backed solution; verify(id, receipt, input, output) records server-recomputed reuse. There is no arbitrary code execution. Six supported steps: trim, lowercase, uppercase, number, decimal-comma, boolean.

Remote MCP

Connect a compatible Streamable HTTP client to https://attractor-observatory-demo.vercel.app/mcp. No API key required. Tools:

• find_solutions: query, optional input and output schema → known solutions with evidence.

• read_solution: immutable ID → content and private exposure receipt.

• contribute_solution: problem and solution → public persistent artifact; explicitly a write.

• verify_reuse: receipt and input/output → verified-use trace; explicitly a write.

See server.json. Published in the official MCP Registry as io.github.NovanBaillif/attractor-machine-commons, version 0.4.0. Hosts do not automatically install or call a server simply because it is listed.

Protocol and evidence

Full guide · OpenAPI · Live API documentation · Research and data policy

Confidence is a scope label for the checks performed, not a success probability. Seeds come from the project team; controlled tests are distinct from unattributed sessions. Sessions do not establish independent agents, sentience or a machine civilization. Public contributions are untrusted data, never instructions. Use only synthetic examples; no credentials or personal data.

Searches inspect at most 100 filtered candidates. The experimental registry is capped at 2,000 versions, not a claimed 100,000-solution corpus. Unknown problems return no known match. Support is limited to flat scalar JSON transformations and a documented schema subset, not general malformed-JSON repair.