Rpcs1 Sdk MCP Server

RPCS-1 SDK — AI Agent Tuner

Configure AI agents that don't oscillate, overload, or freeze.

A configuration framework for AI agents that translates environmental characteristics (entropy, stakes, predictability) into specific LLM parameter recommendations — grounded in RPCS-1 receiver dynamics.

Repository Structure

rpcs1-sdk/
├── packages/core/          # TypeScript recommendation engine (@rpcs1/core)
├── sdk/python/             # Python SDK (pip install rpcs1)
└── .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 pnpm
npm install -g pnpm

# Install dependencies
pnpm install

# Build and test TypeScript core
pnpm --filter @rpcs1/core build
pnpm --filter @rpcs1/core test

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

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

Interactive tuner: https://rpcs1.dev

MCP Server

RPCS-1 is also available as a public, anonymous, read-only MCP server:

https://rpcs1.dev/mcp

It exposes one focused tool:

• recommend_agent_configuration — use when designing, tuning, or diagnosing an AI agent against environmental entropy, predictability, stakes, context horizon, and commitment style.

The MCP surface intentionally wraps the existing deterministic recommendation engine. Broader communication, market, and decision-analysis tools should be added only after their scoring contracts are implemented and tested in the core package.

Discovery metadata:

• OpenAPI: https://rpcs1.dev/openapi.json
• LLM overview: https://rpcs1.dev/llms.txt
• MCP Registry manifest: server.json

Production controls:

• MCP_HOURLY_LIMIT controls per-instance MCP throttling (default: 120 requests per IP/hour).
• MCP_MAX_BODY_BYTES limits request bodies (default: 65536 bytes).
• MCP_ALLOWED_HOSTS is a comma-separated production host allowlist.
• /api/health reports deployment and MCP readiness metadata.

For globally consistent abuse protection across Vercel instances, configure a Vercel Firewall rate-limit rule for /mcp. The in-process limiter is defense in depth, not a distributed quota.

License

MIT