A
licenseA
qualityB
maintenanceDeterministic fluff detector for AI-generated prose. No model, no API key.
3
MIT
OpenWarrant — AI Text Detection
by sh.stipple
Server Details
Probability prose is AI-written, with the tells behind the score. No API key — text or URL in.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
A4.6/5.0
Tool DescriptionsA
Average 4.9/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
Only one tool exists, so there is no ambiguity; the tool's purpose is clearly defined and distinct.
Naming Consistency5/5
With a single tool named 'detect_ai_text', naming is trivially consistent, following a clear verb_noun pattern.
Tool Count3/5
Having just one tool feels thin for a server that references other capabilities (verify_document, verify_references) in its description, but may be acceptable for a narrowly focused detection service.
Completeness2/5
The tool only handles prose text detection and lacks the complementary tools (verify_document, verify_references) mentioned as necessary for non-prose documents and citation verification, leaving significant gaps.
Available Tools
1 tooldetect_ai_textAInspect
Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose).
USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application,
or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this
human-written? detect AI text.
Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s)
link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64
PDF/file, plus `filename` for routing). Returns
`{probability, lean, tells, reasoning, applicable}`.
HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can
false-flag templated/coached or non-native-English writing. It works on PROSE only: for a
form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains,
because AI-text detection false-positives badly there — use `verify_document` (the
authenticity engine) for those, and `verify_references` to check a doc's citations/claims.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| text | No | ||
| filename | No | document.pdf | |
| bytes_b64 | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
A4.9/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that probability is model confidence not calibrated truth, notes false-positive risks for non-native English, and states it abstains on non-prose documents. With no annotations, description fully covers behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections, but slightly verbose. Front-loaded with key info, no wasted sentences.
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 an output schema exists, the description adequately covers input options, usage context, limitations, and alternatives. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description explains the three input methods (text, url, bytes_b64) with context (url is cheapest) and mentions filename. Partially describes output fields.
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 clearly states the tool estimates probability of AI-generated text, specifies the document types (prose like essays, cover letters, etc.), and distinguishes from sibling tools for non-prose documents.
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?
Explicitly says 'USE THIS WHEN' for prose and provides clear exclusion cases (forms, tables, numeric documents) with alternatives (verify_document, verify_references).
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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{
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For server owners:
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