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RPCS-1 Agent Tuner & Translation Bridge

by io.github.travisbergen2

Render a reply for a specific user’s receiver profile

render_reply
Read-onlyIdempotent

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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesYour draft reply.
profileYesThe user’s ReceiverProfile from calibrate_profile.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate readOnlyHint=true and idempotentHint=true. The description adds context by explaining that the tool is deterministic and returns instructions with a 'why-trace,' which further clarifies the read-only, repeatable nature. It does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the core purpose, and every sentence earns its place. It provides the overall role, inputs/outputs, and a concrete action item without any fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description thoroughly explains what the returned instructions cover (structure, warmth, explicitness, revision posture, ambiguity handling) and how to apply them. It also mentions the prerequisite of a calibrated user, connecting to sibling tools in the flow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameters are fully documented. The description merely refers to 'draft reply' and 'user's ReceiverProfile,' which restates the schema. No additional semantic details or examples are provided beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as 'The outbound half of the Translation Bridge loop' and specifies exactly what it does: takes a draft reply and ReceiverProfile, then returns deterministic rendering instructions. It uses a specific verb ('takes... and returns') and distinguishes this tool from siblings by naming its role in the loop.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'Call this on every reply to a calibrated user.' It also instructs to 'Apply the instructions to your draft before answering.' While it doesn't mention when-not-to-use or name alternatives, the placement in the loop makes the usage context clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation2/5

Several tools have overlapping purposes: interpret, prepare_prompt, and route_intent all deal with ambiguity detection and clarification decisions. Their boundaries are subtle (e.g., prepare_prompt returns a commit-vs-clarify decision but defers to route_intent as authority), which will likely confuse an agent trying to select the right tool. The descriptions try to differentiate them, but the functional overlap is significant.

Naming Consistency3/5

The tool names mix conventions: single verbs (interpret, normalize, rewrite) and verb_noun compounds (calibrate_profile, prepare_prompt, render_reply, route_intent). While the verb_noun names are consistent among themselves, the single-verb names break the pattern, giving an overall mixed but readable set.

Tool Count5/5

8 tools is a well-scoped count for the server's purpose (agent tuning and translation bridge). Each tool serves a distinct function in the pipeline without being excessive, and the number is within the ideal 3-15 range.

Completeness4/5

The core lifecycle (calibrate -> prepare -> route -> render) is well-covered, and auxiliary tools (interpret, normalize, rewrite, recommend) fill common side needs. Minor gaps exist, such as no explicit profile update tool and redundancy between prepare_prompt and route_intent, but agents can work around these.

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