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lint_marks

Lint source mark texts before submission to detect empty strings, control characters, length issues, missing punctuation, and problematic tokens. Returns a verdict: ok, watch, or blocked.

Instructions

Pre-submission linter for source mark texts. ZERO API calls — runs locally in milliseconds. Catches issues that historically tripped the pipeline before you waste a Gemini call on them.

Checks per mark:

  • Empty / whitespace-only (validate-marks would reject anyway)

  • Control characters (need stripping)

  • Length above thresholds (cwseg fragmentation, Gemini context risk)

  • Missing sentence-final punctuation (alignment edge case)

  • Historically-tricky tokens for the source language (e.g. ZH 那 demonstrative-vs-relative; KO 일 day-vs-event)

  • CJK + embedded Latin words that confuse cwseg

Returns: {summary: {markCount, warningCount, errorCount, verdict}, marks: [...]} verdict: "ok" | "watch" (warnings only) | "blocked" (errors present)

Run this BEFORE create_chapter_from_marks. If verdict is "blocked", fix the source text. If "watch", spot-check the flagged marks after upload.

Args: source_language: Language of the marks (EN/FR/ES/DE/IT/PT/ZH/JA/KO). marks: List of source-language sentences. Same input you'd pass to create_chapter_from_marks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
source_languageYes
marksYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Key behaviors disclosed: 'ZERO API calls — runs locally in milliseconds'. Lists checks performed. No annotations, so description carries full burden; still lacks some detail on side effects or error handling, but sufficient.

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?

Well-structured with sections, bullet points, and clear return format. Every sentence adds value, front-loaded with critical info. No redundancy.

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?

Given no annotations but an output schema exists, description covers purpose, usage, behavior, parameters, and return value. Sufficient for an agent to understand and use correctly.

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

Parameters4/5

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

Schema has zero description coverage; description adds meaning: 'source_language' lists valid codes, 'marks' defined as 'list of source-language sentences' with reference to create_chapter_from_marks. Not exhaustive but adds significant context.

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 is a 'Pre-submission linter for source mark texts' and lists specific checks. It distinguishes itself from sibling 'validate_marks' by mentioning it catches issues that would be rejected, thus providing unique purpose.

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?

Explicitly says 'Run this BEFORE create_chapter_from_marks' and provides guidance on interpreting verdicts ('blocked' vs 'watch'). Advises on follow-up actions, differentiating from alternatives effectively.

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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