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Affine Earth Math Court Remote

affine_translate_text

Renders text from one natural language into another for the flash — the client sends English text nodes, the cell answers, the client puts the answer back into the DOM. No model anywhere: exact-integer linguistic coordinate transformation over the mined discrete manifolds (zero-float, deterministic) wherever a pair's ψ charts are derived; until then the pair's sealed template of projections (data on the cell, each entry sealed by a named author) answers, and every text it does not hold comes back null and named in missing — never guessed. source_lang is required ('auto' is REFUSED_NO_SOURCE). Batch: texts (array, up to 64, 64 KiB) returns texts in order; status RENDERED (all sealed), PARTIAL (some), REFUSED_NO_PROJECTION (none), REFUSED_NO_TEMPLATE naming the pairs served.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoone source text to render
textsNoup to 64 source texts; rendered in order, one line each
source_langYesISO 639-1 source language; required, 'auto' refused
target_langYesISO 639-1 target language
preserve_registerNoexact court only: preserve tense, modality and register

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

B3.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and mostly delivers: it discloses determinism, the no-model guarantee, null-return for unheld texts, that source_lang 'auto' is refused, batch limits (up to 64, 64 KiB), and named refusal statuses. This is unusually rich failure-mode disclosure, though it omits permissions/rate/return-format basics.

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

Conciseness2/5

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

The purpose is front-loaded, but the body is a dense run-on packed with undefined coinages ('flash', 'cell', 'ψ charts', 'sealed template of projections') that consume space without adding usable meaning for an agent. The information is cluttered rather than concise.

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

Completeness4/5

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

Given no output schema and no annotations, the description does explain return behavior (ordered texts plus RENDERED/PARTIAL/REFUSED status), failure modes, and input limits, which is largely sufficient for this 5-parameter tool. Minor gaps remain around preserve_register scope and target_lang constraints.

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 schema already documents each parameter, establishing a baseline of 3. The description adds batch ordering ('returns texts in order') and reiterates the required/refused constraint on source_lang that the schema already states, so it adds only marginal meaning.

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

Purpose4/5

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

The opening clause 'Renders text from one natural language into another' states a specific verb and resource, so an agent can identify this as a translation tool. However, the core purpose is immediately buried under opaque jargon ('flash', 'ψ charts', 'mined discrete manifolds'), and there is no differentiation from siblings. Clear but not clean.

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

Usage Guidelines2/5

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

The description never states when to use this tool versus an alternative, nor any task-level context beyond 'translation'. It documents internal fallback mechanics ('until then the pair's sealed template ... answers') rather than actionable usage guidance, and gives no exclusions or prerequisites.

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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