Technical Writing Prover MCP Connector for Claude
A+An AI wrote API documentation for 'developers.' No expertise level. No prerequisites. A wall of text with no headings. Code examples that referenced a deprecated method — untested. Passive voice throughout: 'it is recommended that the configuration be updated.' A junior engineer followed the docs, deployed to production with the wrong config, and caused a 4-hour outage. This tool forces audience definition, task-based structure, tested examples, ambiguity elimination, and completeness verification.
AI agents write documentation that reads well — and misleads in production. They write for 'developers' without specifying who. They produce walls of text with no navigation. They include untested code examples. They use passive voice that hides responsibility. They skip error handling, prerequisites, and next steps.
The Problem
LLMs commit five technical writing failures that compound in production:
- Undefined Audience — Written for 'developers' — that is everyone from a CS student to a staff engineer. No expertise level stated. No prerequisites listed. A junior engineer follows senior-level docs, misses implicit assumptions, and deploys a broken config. 67% of documentation complaints cite 'assumed too much knowledge.'
- Absent Structure — A wall of text with no headings, no progressive disclosure. The reader cannot scan to find what they need. No Diátaxis classification — is this a tutorial, a how-to, a reference, or an explanation? The reader does not know, and neither does the author.
- Missing or Untested Examples — 'Something like this' followed by pseudocode that does not compile. Code referencing deprecated APIs. No expected output shown. No runtime version specified. The reader copies the example, and it fails.
- Present Ambiguity — 'It is recommended that the configuration be updated.' Who recommends? Which configuration? Updated how? Passive voice hides the actor. Pronouns without antecedents. Terms used before definition. The reader guesses — and guesses wrong.
- Completeness Gaps — No prerequisites section. No error handling ('if this fails, do X'). No edge cases. No next steps. 'Left as an exercise' and 'consult the official docs' — the documentation fails to document.
How It Works
Technical Writing Prover validates documentation quality through 5 Decision Pivots:
- audienceDefined — Specific reader with role (backend engineer, DevOps, data scientist), expertise level (junior/mid/senior), prerequisites (languages, frameworks they must know), and goal (task accomplished after reading). Not 'developers.'
- structurePresent — Diátaxis classification (tutorial/how-to/reference/explanation), task-based heading hierarchy (H1→H2→H3), progressive disclosure (simple → advanced), scannable navigation. Not wall-of-text.
- examplesWorking — Complete, copy-pasteable code blocks with language in fence, expected output shown, tested on stated runtime version. Not 'something like this' or pseudocode.
- ambiguityEliminated — Active voice ('use X' not 'it is recommended'), clear pronoun antecedents, terms defined on first use, vague quantifiers replaced with specifics. Not 'various factors.'
- completenessVerified — Prerequisites listed with version numbers, error paths documented ('if this fails, do X'), edge cases covered, next steps linked. Not 'beyond the scope.'
The Verdict Matrix
| First Failing Pivot | Verdict | Meaning |
|---|---|---|
| audienceDefined = false | AUDIENCE_UNDEFINED | 'Developers' is everyone. No specific reader targeted. |
| structurePresent = false | STRUCTURE_ABSENT | Wall of text. No headings, no navigation, no Diátaxis. |
| examplesWorking = false | EXAMPLES_MISSING | Pseudocode or untested code. Reader copies and fails. |
| ambiguityEliminated = false | AMBIGUITY_PRESENT | Passive voice, unclear pronouns, undefined terms. |
| completenessVerified = false | COMPLETENESS_GAPS | Missing prerequisites, error handling, or next steps. |
| All pivots pass | WRITING_PROVEN | Defined, structured, exemplified, clear, complete. |
Why It Works
- Tool calls are obligations. The agent cannot skip audience definition or claim completeness without listing prerequisites and error paths. Filling the fields IS the documentation work.
- Consistency engine catches contradictions. If the agent claims
examplesWorking=truebut describes 'pseudocode,' the engine rejects with a specific coaching message. - Semantic traps detect hand-waving. Phrases like 'developers,' 'left as an exercise,' 'it is recommended,' or 'something like this' trigger automatic rejection. Name the audience. Write the example. State the subject.
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