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Glama

RPCS-1 Agent Tuner & Translation Bridge

by io.github.travisbergen2

Fork view — how could this message read?

fork
Read-onlyIdempotent

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 forked-answer scaffold. Silent on clean text by contract. Runs the deterministic mirror floor only over MCP (no model). Prefer this over interpret for span-level ambiguity detection: interpret’s entity list is a word-list engine (calibrated 2026-08-15: no discrimination on conversational text) — advisory only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe message to analyze for forks.
rejectedNoReading summaries the user already rejected — never re-offered.

Schema Changelog

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

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses that it is deterministic ('no model'), silent on clean text by contract, and returns specific artifacts (competing readings, ask-back, scaffold). These are non-obvious behaviors not inferable from annotations.

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

Conciseness4/5

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

The description is dense but not bloated; each sentence adds a distinct piece of information. It is front-loaded with the core purpose, and jargon like 'calibrated ambiguity surface' is acceptable given the domain. Slight over-elaboration in the interpret comparison keeps it from a 5.

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?

The description covers what the tool returns, when it stays silent, that it is deterministic and model-free, and how it differs from a sibling. Together with comprehensive annotations and schema, the agent has a complete picture for selection and invocation.

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 coverage is 100% for both parameters (text and rejected), with clear descriptions. The tool description adds context about output scaffolds but does not elaborate on parameter behavior beyond what the schema already specifies. Baseline 3 is appropriate.

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 states the tool detects structural forks (ambiguities) with character-offset spans, returns competing readings, an ask-back question, and a forked-answer scaffold. It distinguishes itself from interpret by explicitly saying 'Prefer this over interpret for span-level ambiguity detection'.

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

Usage Guidelines5/5

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

It provides explicit guidance: 'Prefer this over interpret for span-level ambiguity detection' and explains interpret is advisory only. The contract 'Silent on clean text' gives clear when-not-to-use context, and the deterministic, no-model nature informs expectations.

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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