avoid-ai-writing-mcp

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Audit text for AI-writing patterns

audit_text

Read-onlyIdempotent

Run a local heuristic audit of text to detect AI-writing patterns, returning a score, up to 100 flagged patterns, and highlighted sentence regions. No network calls are used.

Instructions

Audit text locally with the deterministic Avoid AI Writing detector. Returns the score plus up to 100 flagged patterns and 100 highlighted sentence regions, with truncation counts. This is a heuristic writing audit, not proof of authorship; use score_text for a compact result.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	Text to evaluate locally. The text is not sent to any network service.
`context`	No	Writing context. Technical mode suppresses patterns common in code-adjacent prose; defaults to general.	general

Output Schema

TableJSON Schema

Name	Required	Description	Default
`label`	Yes
`score`	Yes
`issues`	Yes
`context`	Yes
`scorable`	Yes
`truncated`	Yes
`confidence`	Yes
`highlights`	Yes
`statistics`	Yes
`word_count`	Yes
`issue_count`	Yes
`probabilities`	Yes
`classification`	Yes
`unscored_reason`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedAug 23, 2026

addedOutput schema / properties / highlights / maxItems
```
Added value: +100
```
addedOutput schema / properties / issues / maxItems
```
Added value: +100
```

addedOutput schema / properties / truncated

Added value: +{
+  "additionalProperties": false,
+  "properties": {
+    "highlights": {
+      "maximum": 9007199254740991,
+      "minimum": 0,
+      "type": "integer"
+    },
+    "issues": {
+      "maximum": 9007199254740991,
+      "minimum": 0,
+      "type": "integer"
+    }
+  },
+  "required": [
+    "issues",
+    "highlights"
+  ],
+  "type": "object"
+}

changedOutput schema / required

Previous value: -[
-  "score",
-  "label",
-  "scorable",
-  "unscored_reason",
-  "classification",
-  "confidence",
-  "probabilities",
-  "word_count",
-  "issue_count",
-  "context",
-  "issues",
-  "statistics",
-  "highlights"
-]New value: +[
+  "score",
+  "label",
+  "scorable",
+  "unscored_reason",
+  "classification",
+  "confidence",
+  "probabilities",
+  "word_count",
+  "issue_count",
+  "context",
+  "issues",
+  "statistics",
+  "highlights",
+  "truncated"
+]

First observedv0.1.0Aug 22, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds valuable behavior beyond that: the audit runs locally, is deterministic, returns up to 100 flagged patterns and 100 highlighted sentence regions, and includes truncation counts. It also disclaims that the result is heuristic rather than proof, which is essential context for the agent's interpretation.

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?

Three sentences, each earning its place: one identifies the action and method, one lists the return bounds and truncation behavior, and one sets expectations and routes to the sibling. The most decision-relevant facts are front-loaded.

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 output schema exists, the annotations cover safety, and the description covers local processing, deterministic behavior, response bounds, truncation counts, and the heuristic caveat, an agent has everything it needs to select and invoke the tool correctly. The only thing left to the schema is the context enum, which is appropriately delegated.

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 the text and context parameters are already documented. The description adds no extra parameter semantics, but it doesn't need to because the schema carries that weight. Baseline 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 action ('Audit text'), names the concrete detector ('the deterministic Avoid AI Writing detector'), and distinguishes this tool from its sibling score_text by describing the richer audit output. An agent can tell exactly what this tool does and how it differs from the sibling without opening the schema.

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 gives clear usage context: it is a local, deterministic, heuristic writing audit and not proof of authorship. It explicitly says to use score_text when a compact result is needed, naming the alternative and the condition that selects it.

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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