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conorbronsdon

avoid-ai-writing-mcp

Score text for AI-writing patterns

score_text
Read-onlyIdempotent

Evaluate text locally for AI-writing patterns, returning a deterministic 0-100 score, classification, confidence, and counts. Use the result to decide whether deeper auditing is needed.

Instructions

Score text locally with the deterministic Avoid AI Writing detector. Returns a compact 0-100 pattern score, classification, confidence, and counts. This is a heuristic signal, not proof of authorship; use audit_text when individual findings are needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to evaluate locally. The text is not sent to any network service.
contextNoWriting context. Technical mode suppresses patterns common in code-adjacent prose; defaults to general.general

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelYes
scoreYes
contextYes
scorableYes
confidenceYes
word_countYes
issue_countYes
probabilitiesYes
classificationYes
unscored_reasonYes
Behavior5/5

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

Annotations already cover read-only, idempotent, and non-destructive hints. The description adds critical behavioral traits: the detector is deterministic, it is a heuristic signal not proof of authorship, and it operates locally. These go beyond the annotations and help calibrate trust in the output.

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?

Two sentences with no filler. The action and scope are front-loaded, the caveat is stated compactly, and the alternative is offered in the same breath. Every word earns its place.

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?

The tool has a rich output schema, complete parameter documentation, and strong annotations. The description covers the operation's purpose, the caveat about heuristic results, and the routing to audit_text. Nothing an agent needs to call it correctly is missing.

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% – both parameters have clear descriptions (text is local, context has default and behavior). The description itself does not add parameter-specific semantics beyond the schema, so the baseline 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 states a specific verb ('Score'), a resource ('text'), and the exact detector ('Avoid AI Writing'). It also names the sibling tool 'audit_text' and differentiates by saying when audit_text is needed, so it clearly distinguishes from the alternative.

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 explicitly says to 'use audit_text when individual findings are needed', implying this tool is for a compact score. It also notes the operation is local and deterministic, giving clear context for when to choose this tool.

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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