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check_citation_integrity

Detect verbatim word-runs of 3+ words shared between a draft passage and source texts to verify citation integrity and flag potential paraphrase issues.

Instructions

Flag verbatim word-runs of 3+ words shared between a draft passage and one or more source texts — a real paraphrase check, not AI-detection evasion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draft_textYes
source_textsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral disclosure burden. It reveals the core algorithm, the exact 3-word threshold, and the boundary that this is not an AI-detection bypass tool. It does not describe the output format, but the behavior itself is stated specifically and honestly.

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?

The description is a single, front-loaded sentence with no filler. The verb, object, and rule appear first, and the contrast clause 'not AI-detection evasion' earns its place by preventing misuse.

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?

For a low-complexity tool with two plain parameters and no output schema, the description covers the input semantics, the matching rule, and the intended use. It omits an explicit return format, but 'Flag...' implies the result is the flagged overlap, so the gap is minor.

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 0%, so the description must compensate. It lightly maps draft_text to 'draft passage' and source_texts to 'one or more source texts,' but it does not detail parameter expectations such as length limits, plain-text assumptions, or normalization rules. The parameter names are self-explanatory, so this is adequate but not rich.

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 starts with a specific verb ('Flag') and identifies the exact resource and rule: verbatim word-runs of 3+ words shared between a draft passage and source texts. It also distinguishes itself from AI-detection evasion, making the tool's purpose and non-purpose immediately clear.

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

Usage Guidelines4/5

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

The description clearly implies the tool is for real paraphrase assessment and explicitly says it is 'not AI-detection evasion,' which works as a when-not-to-use signal. It does not name sibling alternatives or provide formal usage conditions, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.