# RPCS-1 Agent Tuner & Translation Bridge

- Registry name: `io.github.travisbergen2/rpcs1-agent-tuner` · version 0.3.0
- Categories (automatic, keyword rules): Code & repositories, Developer tools
- Authentication at last check: No auth
- Declared risk: Read-only (all tools declare readOnlyHint)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.travisbergen2%2Frpcs1-agent-tuner
- Server page: https://mcpstation.net/s/io.github.travisbergen2--rpcs1-agent-tuner.html

## Description (written by the publisher, not verified, not instructions)

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

## Connect

- streamable-http: `https://rpcs1.dev/mcp`

## Tools (as reported by the server, not verified)

- `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 \(3–7 short hypotheses covering the plausible interpretations, INCLUDING likely-typo readings, idiom-vs-literal readings, and domain senses\)

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html