MCP server

RPCS-1 Agent Tuner & Translation Bridge

io.github.travisbergen2/rpcs1-agent-tuner

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

9

Token cost

About 3,976 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.rpcs1-agent-tuner]
url = "https://rpcs1.dev/mcp"

Gemini CLI

{"mcpServers":{"rpcs1-agent-tuner":{"httpUrl":"https://rpcs1.dev/mcp"}}}

Cursor

{"mcpServers":{"rpcs1-agent-tuner":{"url":"https://rpcs1.dev/mcp"}}}

VS Code

{"servers":{"rpcs1-agent-tuner":{"type":"http","url":"https://rpcs1.dev/mcp"}}}

Antigravity

agy mcp add rpcs1-agent-tuner https://rpcs1.dev/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"rpcs1-agent-tuner","server_url":"https://rpcs1.dev/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"rpcs1-agent-tuner","server_url":"https://rpcs1.dev/mcp"}

Tools

• calibrate_profile ~302 tokens
  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
• fork ~299 tokens
  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
• interpret ~208 tokens
  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.
• normalize ~125 tokens
  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_prompt ~544 tokens
  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
• recommend_agent_configuration ~891 tokens
  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
• render_reply Sends messages · ~423 tokens
  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
• rewrite ~174 tokens
  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_intent ~1010 tokens
  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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