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write_report

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Generate a shareable Markdown report with AI-detection evidence, including score, flagged signals, character locations, citation mismatches, and error-rate context for offline review.

Instructions

Produces the full analysis as a Markdown document a person can keep, forward to a writer, or take to an academic-integrity committee — the finished artefact rather than a summary to paraphrase. It contains the score, the signals that counted and the ones found at a rate people write at, the characters found in the file with their line and column, and the places where the document's citations disagree with its own bibliography. Checkable facts are named at the top and kept apart from the score, which is an opinion about prose. Every report prints how often this build is wrong, measured for the language actually analysed against texts written before 2022, and names the rules known to fire on human writing so the reader can weigh evidence that leans on one. Below the threshold that measurement supports, no verdict is given at all. Runs FULLY OFFLINE. The result contains material from the document, so treat it as you would the coursework itself: hand it to the person who asked, do not post it anywhere. Prefer this over paraphrasing the other tools' output when the user wants something to send, save, print or attach.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe document to analyse and describe.
titleNoTitle for the document. Defaults to a title in the report language.
languageNo"en", "es", or "auto" to detect. Defaults to auto.
documentNameNoName of the file or assignment, printed on the report. Optional.
interfaceLanguageNoReader-facing report language: "en" or "es". Independent from the analysed text. Defaults to English.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that the tool runs fully offline, that the output contains document material (privacy caution), and that below a threshold no verdict is given. It also clarifies that the score is an opinion, separate from checkable facts. The readOnlyHint annotation is consistent with the offline read-only nature, and the description adds operational details well beyond the annotation.

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?

Though the description is long, every sentence adds distinct value: purpose, output contents, caveats about evidence, offline nature, privacy, and usage preference. It is front-loaded with the primary purpose and structured logically, with no redundancy. The length is justified by the tool's complexity.

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 the complexity of the tool (five parameters, no output schema), the description thoroughly explains what the returned report contains, including the distinction between facts and opinion, the threshold behavior, and the offline constraint. It also covers edge cases like citation disagreements. No essential information is missing for an agent to correctly invoke and interpret the output.

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 all five parameters (text, title, language, documentName, interfaceLanguage) are already documented in the schema with clear descriptions. The tool description does not add any additional parameter-specific guidance, but the schema is sufficient, so a baseline score of 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 opens with a specific verb ('Produces') and resource ('full analysis as a Markdown document'), and immediately distinguishes the output from a summary. It names concrete content elements (score, signals, characters, citation disagreements) and contrasts with siblings like inspect_characters and check_citations. This clearly separates it from the other tools.

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?

The description ends with an explicit directive: 'Prefer this over paraphrasing the other tools' output when the user wants something to send, save, print or attach.' This gives clear when-to-use guidance and implicitly warns against using it for summaries, making the decision explicit.

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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