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RPCS-1 Agent Tuner & Translation Bridge MCP server

online · 100% uptime
by travisbergen2·io.github.travisbergen2/rpcs1-agent-tuner·v0.3.0
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Find your AI agent's likely failure mode, get runtime settings, and clarify ambiguous prompts.

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A91/100

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RPCS-1 Agent Tuner & Translation Bridge tools (9)

write = sends, deletes, buys or posts
  • calibrate_profileFree

    Build a ReceiverProfile (TI, SG, FT, UE, AR — continuous 0-100, never a category label) from five behavioral forced-choice answers. Call with NO answers to get the five questions to ask the user; call again with their answers (a/b/c per primitive) to get the profile. Store the returned profile JSON in the user’s notes or memory and pass it to render_reply / prepare_prompt on every turn. Deterministic and stateless — nothing is stored server-side. Schema: https://rpcs1.dev/v1/receiver-profile.json

  • forkFree

    The calibrated ambiguity surface: deterministic structural fork detectors (reference, scope, grouping, compare-vs-choose, polysemy) with character-offset spans, plus per-reading one-line clarifiers the sender can append to lock a reading in. Returns competing readings, an ask-back question, and a forked-answer scaffold. Silent on clean text by contract. Runs the deterministic mirror floor only over MCP (no model). Prefer this over interpret for span-level ambiguity detection: interpret’s entity list is a word-list engine (calibrated 2026-08-15: no discrimination on conversational text) — advisory only.

  • interpretFree

    Detect ambiguity in user messages using the RPCS-1 Signature Ambiguity Framework. Returns AR level (AR0-AR5), confidence, candidate interpretations with scores, clarifying questions, and suggested next step. Use when a user says something vague, passive-aggressive, or underspecified.

  • normalizeFree

    Clean up text with ellipses, fragments, and run-on thoughts into coherent prose. Use when a user types stream-of-consciousness or fragmented input.

  • prepare_promptFree

    The inbound half of the Translation Bridge loop. Takes the user’s raw message (possibly ambiguous, fragmented, or underspecified) plus their ReceiverProfile, and returns the recovered intent, a canonical translation to act on, ambiguity level, and — profile-aware — whether to clarify or commit. Call this before acting on any ambiguous user request. Scope note: its detectors are lexical/structural (vague signals, ambiguous references) — for the commit-vs-clarify DECISION, route_intent (with your own proposed readings) is the authority; when they disagree, follow route_intent.

  • recommend_agent_configurationFree

    Diagnose why a deployed AI agent may fail. Takes environmental entropy, predictability, stakes, context horizon, and commitment style, then returns receiver profile values (TI, SG, FT, UE, AR), platform parameters (temperature, top_p, strategy), regime prediction, reasoning, and warnings. Optionally pass target_model (the actual model id) to attach MEASURED per-model receiver posture (E-LIT table): evidence-graded literalness, truth-override boundary, and translation directives. Deterministic, stateless, read-only — does not store past recommendations.

  • render_replyFree

    The outbound half of the Translation Bridge loop. Takes your draft reply plus the user’s ReceiverProfile and returns deterministic rendering instructions (structure, warmth, explicitness, revision posture, ambiguity handling — each with a why-trace). Apply the instructions to your draft before answering. Call this on every reply to a calibrated user.

  • rewriteFree

    Get rewrite instructions for adapting text to a specific style: technical, plain, socially_gentle, concise, detailed, or direct. Use when communication needs tone adjustment.

  • route_intentFree

    Entropy routing over competing interpretations — the model proposes, the deterministic core disposes. YOU generate the candidate readings of the user’s message (3–7 short hypotheses covering the plausible interpretations, INCLUDING likely-typo readings, idiom-vs-literal readings, and domain senses) and pass them as hypotheses, ideally with your own likelihoods (0–1 per reading) AND a paraphrase per reading — the user’s message rewritten unambiguously under that interpretation, so the user can VERIFY intent by recognition before anything commits (one misread prompt skews a whole thread). The router computes the posterior and its normalized entropy T̂ and returns the decision: commit (one reading dominates), commit_with_note (close alternative disclosed), present_options (several readings live), or clarify (ask before acting — open-endedly when nothing discriminates). Thresholds adapt to the user’s ReceiverProfile (AR widens/narrows the commit region; high FT discloses near-ties). This tool is the commit-vs-clarify AUTHORITY in the pipeline. Omitting hypotheses falls back to a generic six-intent PRODUCT-ROUTING starter set — do not use the fallback for interpreting arbitrary sentences. Deterministic, stateless, read-only. Benchmarked: RTEB v1.1 (developer-bench grade; see docs/routing.md).

Public scan report

scanner v0.1.9 · 2026-09-24 · same rubric, same numbers if you re-run it

no findings
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 1097ms20/20
  • Tool poisoning9 tool descriptions checked15/15
  • Auth qualityopen endpoint, read-only tools10/15
  • Maintenancelast push 12 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 91/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How RPCS-1 Agent Tuner & Translation Bridge compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
91/100
higher than 88% of 4,460 AI and agents servers
Tools
9
the typical AI and agents server has 6
Tools that change things
0%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote AI and agents server answers 100%
Response time
302 ms
faster than 47% of 2,331 AI and agents servers

What its tools let an agent do: 8 read · 1 create

Install RPCS-1 Agent Tuner & Translation Bridge in Claude Code, Cursor or VS Code

claude mcp add --transport http rpcs1-agent-tuner https://rpcs1.dev/mcp
Add to Cursor

What the publisher says

From the RPCS-1 Agent Tuner & Translation Bridge repository's README, as published. We do not edit it. Read it on GitHub

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.

Nothing above is checked by us. What we check is on the safety report.

RPCS-1 Agent Tuner & Translation Bridge: common questions

Is RPCS-1 Agent Tuner & Translation Bridge MCP server safe?
Yes, by our scan: it is graded A (91/100). Read the RPCS-1 Agent Tuner & Translation Bridge safety report
How do I install RPCS-1 Agent Tuner & Translation Bridge?
It runs remotely at rpcs1.dev. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does RPCS-1 Agent Tuner & Translation Bridge need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is RPCS-1 Agent Tuner & Translation Bridge maintained?
The last commit was 53 days ago (2026-09-12). The latest release is v0.3.0.
Is RPCS-1 Agent Tuner & Translation Bridge up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of RPCS-1 Agent Tuner & Translation Bridge?
Servers from other publishers that do the same job: Agent-Native Analytics MCP server, Image MCP server and llmtrim MCP server.
How does RPCS-1 Agent Tuner & Translation Bridge compare with other AI and agents MCP servers?
safety score 91/100 (higher than 88% of 4,460 AI and agents servers); tools 9 (the typical AI and agents server has 6); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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