RPCS-1 Agent Tuner & Translation Bridge

io.github.travisbergen2/rpcs1-agent-tuner · v0.3.0

Find your AI agent's likely failure mode, get runtime settings, and clarify ambiguous prompts.

MCPremoteno credentialsMessaging

Reported outcomes

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Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 9 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

A 99/100

What it does: 9 tools · 9 read, 0 write · Messaging, AI & LLM, Knowledge & docs

Test call recommend_agent_configuration: worked (1,022 ms)

How the rating is made

Answers without login: +30
Speed of first answer: +10
Lists tools: +10
Tool descriptions: +15
Parameter descriptions: +9
Read/write annotations: +5
Safe test call: +20
More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL

https://rpcs1.dev/mcp

Tools

9 tools listed at last check

recommend_agent_configuration 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
interpret 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.
fork 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 fo
normalize Clean up text with ellipses, fragments, and run-on thoughts into coherent prose. Use when a user types stream-of-consciousness or fragmented input.
rewrite Get rewrite instructions for adapting text to a specific style: technical, plain, socially_gentle, concise, detailed, or direct. Use when communication needs tone adjustment.
calibrate_profile 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
prepare_prompt 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
render_reply 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 answe
route_intent 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)

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