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glim.sh

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AI Detection

glim_detect_ai
Read-onlyIdempotent

Detect AI-generated text. Scores any text for AI-authorship likelihood and returns an overall verdict (AI / human / mixed) with confidence, the AI/human/AI-assisted fractions, and a segment-by-segment breakdown showing exactly which parts read as AI-written - including per-segment humanizer flags (AI output run through paraphrasing/'humanizer' tools). Use it to verify whether content (comments, articles, profiles) is AI-generated, or to check text before publishing to see which segments would trip AI detectors - revise the flagged segments and re-check. Cost scales with text length: $0.06 per 100 words, rounded up, minimum $0.06. Max input 20,000 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to analyze, plain text, 50+ characters, max 20,000. Detection reliability improves with length; very short texts return lower-confidence verdicts.
formatNo'text' (default): compact human-readable report. 'json': structured data incl. per-segment text, scores, and humanizer flags.text

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / format / description
      Previous value: -"'text' (default): compact human-readable report. 'json': structured data incl. per-segment scores and raw detector windows."New value: +"'text' (default): compact human-readable report. 'json': structured data incl. per-segment text, scores, and humanizer flags."
    • changedInput schema / properties / text / description
      Previous value: -"Text to analyze, plain text, 50+ characters. Max 40,000 chars (standard tier) / 20,000 chars (premium). Detection reliability improves with length; very short texts return lower-confidence verdicts."New value: +"Text to analyze, plain text, 50+ characters, max 20,000. Detection reliability improves with length; very short texts return lower-confidence verdicts."
    • removedInput schema / properties / tier
      Removed value: -{
-  "default": "standard",
-  "description": "Detection class. 'standard': fast, cheap, accurate for routine checks. 'premium': highest accuracy, additionally flags humanized text (AI output run through paraphrasing/'humanizer' tools), and is substantially more robust to evasion; ~10x the price.",
-  "enum": [
-    "standard",
-    "premium"
-  ],
-  "type": "string"
-}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this as readOnly, idempotent, open-world, and non-destructive. The description adds substantial behavioral context beyond that: cost scaling ($0.06 per 100 words, minimum $0.06), max input length of 20,000 characters, minimum text length of 50 characters, reliability degradation on short texts, and the difference between text and json output formats.

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 front-loaded with the core purpose, then methodically covers outputs, use cases, pricing, and limits. Every sentence provides actionable information with no filler or repetition.

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?

Even without an output schema, the description fully explains the return values (verdict, confidence, fractions, segment breakdown, humanizer flags), the two format options, input constraints, cost model, and length-based reliability caveat. An agent has everything needed to select and invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so a baseline of 3 applies. The description adds value by clarifying what each output format yields (compact human-readable report vs structured data with per-segment text, scores, and humanizer flags) and reaffirming the length-dependent reliability guidance for the text parameter.

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 and resource ('Detect AI-generated text') and details the exact outputs: verdict, confidence, fractions, segment breakdown, and humanizer flags. It clearly differentiates this tool from the sibling get/search/fetch tools, which serve different resource types.

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 explicitly names two use cases: verifying whether content is AI-generated and pre-publishing checks to find segments that would trip AI detectors. It stops short of stating when not to use the tool or naming alternatives, but given the sibling list contains no AI-detection tool, the usage context is clear.

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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