# RPCS-1 Agent Tuner & Translation Bridge (remote · rpcs1.dev)

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

- Trust score: 67/100 (medium)
- Change this week: +8
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `rpcs1.dev`: 67/100 (this document), [markdown](https://verifymcp.io/servers/travisbergen2-rpcs1-agent-tuner/rpcs1.md), [page](https://verifymcp.io/servers/travisbergen2-rpcs1-agent-tuner/rpcs1)

## Channel facts

- Endpoint: `https://rpcs1.dev/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.3.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 31/100
  - AI-judged instruction clarity (poor).
  - Context-footprint check failed: tool/resource definitions use about 1562 tokens (~195/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 97/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 89% of tool parameters carry a description.
  - Structured output schemas are declared (13% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http travisbergen2-rpcs1-agent-tuner https://rpcs1.dev/mcp
```

### Codex

```toml
[mcp_servers.travisbergen2-rpcs1-agent-tuner]
url = "https://rpcs1.dev/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "travisbergen2-rpcs1-agent-tuner": {
      "type": "remote",
      "url": "https://rpcs1.dev/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add travisbergen2-rpcs1-agent-tuner --url https://rpcs1.dev/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  travisbergen2-rpcs1-agent-tuner:
    url: "https://rpcs1.dev/mcp"
```

### Other

```json
{
  "mcpServers": {
    "travisbergen2-rpcs1-agent-tuner": {
      "type": "http",
      "url": "https://rpcs1.dev/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-01 (score 66, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 65, +5)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 60, +1)

- [security] Tool “route_intent” rewrote its description, which is the text the model reads
- [functional] Server version: 0.4.1 → 0.4.2

### 2026-07-29 (score 59, −1)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “prepare_prompt” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 139 → 190
- [functional] Server version: 0.3.1 → 0.4.1
- [functional] New tool “route_intent”

### 2026-07-28 (score 60, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 59, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 58)

First indexed and scored.

## MCP tools (8)

### `recommend_agent_configuration` (~234 tokens)

Recommend AI 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 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.

Input parameters:

- `environment` (object)
- `target_model` (string): Optional: the actual model id this agent will run on (e.g. "claude-sonnet-4-6", "deepseek-v4-pro"). When it matches a measured per-model receiver entry (E-LIT table), measured translation directives…
- `target_platform` (string): The platform whose runtime parameters should be recommended.
- `task` (object)

Output parameters:

- `confidence` (string)
- `imm_principles_applied` (array)
- `platform_parameters` (object)
- `predicted_regime` (string)
- `reasoning` (string)
- `receiver_profile` (object)
- `warnings` (array)

### `interpret` (~89 tokens)

Interpret ambiguous human input

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.

Input parameters:

- `risk` (string): Risk category for ambiguity threshold.
- `text` (string, required): The message to interpret.

### `normalize` (~50 tokens)

Normalize fragmented human input

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

Input parameters:

- `text` (string, required): Fragmented text to normalize.

### `rewrite` (~61 tokens)

Rewrite text for a target audience

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

Input parameters:

- `style` (string): Target audience style.
- `text` (string, required): Text to rewrite.

### `calibrate_profile` (~147 tokens)

Calibrate a user’s receiver 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 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

Input parameters:

- `answers` (object): Chosen option id per primitive. Omit entirely to receive the questions.

### `prepare_prompt` (~177 tokens)

Prepare a user’s message before acting on it

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.

Input parameters:

- `profile` (object): The user’s ReceiverProfile from calibrate_profile. Shapes clarify-vs-commit behavior.
- `risk` (string): Risk category for the ambiguity threshold.
- `text` (string, required): The user’s raw message.

### `render_reply` (~98 tokens)

Render a reply for a specific user’s receiver profile

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.

Input parameters:

- `profile` (object, required): The user’s ReceiverProfile from calibrate_profile.
- `text` (string, required): Your draft reply.

### `route_intent` (~374 tokens)

Route an ambiguous request: commit, present options, or clarify

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).

Input parameters:

- `hypotheses` (array): Candidate interpretations. Omit to use a generic six-intent starter set plus a catch-all.
- `likelihoods` (object): Optional externally computed likelihood per hypothesis id (e.g. model-derived) — replaces the lexical scorer.
- `profile` (object): The user’s ReceiverProfile from calibrate_profile. Shapes commit-vs-clarify thresholds.
- `text` (string, required): The user’s raw message.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/travisbergen2-rpcs1-agent-tuner/rpcs1#diagnostics

## Score history

- 2026-08-03: 67
- 2026-08-02: 66
- 2026-08-01: 66
- 2026-07-31: 65
- 2026-07-30: 60
- 2026-07-29: 59
- 2026-07-28: 60
- 2026-07-27: 59
- 2026-07-26: 58

## Links

- Remote endpoint: https://rpcs1.dev/mcp
- Repository: https://github.com/travisbergen2/rpcs1-sdk
- Website: https://rpcs1.dev/tuner?utm_source=official-mcp-registry&utm_medium=mcp-directory&utm_campaign=listing
- Changelog RSS feed: https://verifymcp.io/servers/travisbergen2-rpcs1-agent-tuner/rpcs1/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/travisbergen2-rpcs1-agent-tuner/rpcs1/changelog.json
- HTML version of this page: https://verifymcp.io/servers/travisbergen2-rpcs1-agent-tuner/rpcs1