Explicit Formula — Second Brain MCP server

RPCS-1 SDK — AI Agent Tuner

<!-- mcp-name: io.github.travisbergen2/rpcs1-agent-tuner -->

Start with the free tuner: find your AI agent’s likely failure mode, get runtime settings to try, and validate them with a harder case.

RPCS-1 helps teams make agent settings deliberate rather than guessed. Describe the task, change rate, predictability, stakes, relevant context horizon, and commitment style; it returns a five-primitive profile, a runtime recommendation, and a next test. The suite also includes SendRight for catching ambiguous prompts before handoff and the Translation Bridge for profile-aware communication rendering.

Repository Structure

rpcs1-sdk/
├── packages/core/          # TypeScript engine (@rpcs1/core): tuner + translation layer + receiver-profile intake
├── packages/web/           # Next.js app serving rpcs1.dev (tuner, translator, docs, Stripe, /mcp endpoint)
├── packages/mcp-server/    # Standalone STDIO MCP server (what Glama and MCP clients build)
├── sdk/python/             # Python SDK (pip install rpcs1)
├── skills/                 # Canonical agent skill package (HF-HATP v2.0 SKILL.md)
├── docs/                   # Architecture, deployment, launch playbook
└── .github/workflows/      # CI/CD

Quick Start — Python SDK

pip install rpcs1

from rpcs1 import recommend_params

config = recommend_params(
    task_description="Customer support agent",
    environment_entropy="dynamic",
    environment_predictability="somewhat_predictable",
    stakes="high",
    target_platform="anthropic",
)

print(config.platform_parameters.temperature)   # e.g. 0.52
print(config.predicted_regime)                  # 'stable'
print(config.reasoning)                         # cites Matching Principle

Quick Start — TypeScript Core

import { recommend } from '@rpcs1/core';

const rec = recommend({
  task: { task_summary: 'Customer support agent' },
  environment: {
    entropy: 'dynamic',
    predictability: 'somewhat_predictable',
    stakes: 'high',
    context_relevance: 'medium',
    commitment_style: 'cautious',
  },
  target_platform: 'anthropic',
});

console.log(rec.platform_parameters.temperature);
console.log(rec.predicted_regime);

Development

# Install dependencies
npm ci --include=optional

# Build and test TypeScript core
npm run build --workspace=@rpcs1/core
npm run test --workspace=@rpcs1/core

# Test Python SDK
cd sdk/python
pip install -e ".[dev]"
pytest -v

Web environment variables are documented in packages/web/.env.example (Stripe, Resend, license signing, rate limits). MCP production controls are listed under Production controls below.

The web app deploys to Vercel on Node 24 (region iad1); pushes to main trigger the production deployment.

The Matching Principle

The SDK implements Pred-09-5 from IMM Paper 9:

Stable receivers in an environment with entropy H satisfy TI ~ 1/H.

High-entropy environments → short attention windows (TI ~ 10). Low-entropy environments → long attention windows (TI ~ 90).

Every parameter recommendation traces back to this principle or the basin stability geometry (oscillation/overload/freeze boundary conditions).

Web App

• Free Tuner: https://rpcs1.dev/tuner
• SendRight: https://rpcs1.dev/send
• Translation Bridge: https://rpcs1.dev/translator
• Calibrate a communication-preference profile: https://rpcs1.dev/calibrate

The site can also explain the same product facts in technical, executive, plain-language, or literal-and-precise registers. The explanation changes; pricing, deliverables, and limitations do not.

Brand — Explicit Formula (product) / RPCS-1 (mechanism)

Shortened. The full README is on GitHub.