RPCS-1 Agent Tuner & Translation Bridge
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| recommend_agent_configurationA | 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 to attach MEASURED per-model receiver posture (E-LIT table). Deterministic, stateless, read-only — does not store past recommendations. |
| interpretA | Detect ambiguity in a user message and score candidate interpretations using the RPCS-1 Signature Ambiguity Framework. Returns literal summary, implied meaning, confidence, AR level (AR0-AR5), ambiguities, clarifying questions, and per-candidate scores (IC, UE, EC, NM, SG, TI). Use when a user says something vague, passive-aggressive, or underspecified. |
| normalizeA | Clean up text with ellipses, fragments, and run-on thoughts into coherent prose. Returns the number of fragments detected and the joined version. Use when a user types stream-of-consciousness or fragmented input. |
| rewriteA | Get rewrite instructions for adapting text to a specific audience style: technical, plain, socially_gentle, concise, detailed, or direct. Pass the result to an LLM with the rewrite_instructions as the system prompt. Use when communication needs tone adjustment. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: recommend_agent_configuration handles diagnostics, interpret detects ambiguity, normalize cleans text, and rewrite adjusts tone. No overlap in functionality.
All tool names follow a consistent verb_noun pattern (recommend_agent_configuration, interpret, normalize, rewrite), with descriptive suffixes where needed. No mixing of conventions.
Four tools is a well-scoped set for an agent tuner and translation bridge, covering diagnostics, ambiguity resolution, text normalization, and style adaptation without excess or deficiency.
The tool set lacks a direct translation feature despite the server name. Additionally, the rewrite tool only provides instructions, not actual output, creating a dependency on an external LLM. Core operations are present but notable gaps exist.