OpenWarrant — AI Text Detection
Server Details
Detect AI-generated text: the probability a document's prose was LLM-written, with the tells.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.9/5 across 1 of 1 tools scored.
Only one tool exists, so there is no ambiguity; the tool's purpose is clearly defined and distinct.
With a single tool named 'detect_ai_text', naming is trivially consistent, following a clear verb_noun pattern.
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.
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.| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| text | No | ||
| filename | No | document.pdf | |
| bytes_b64 | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses that the probability is confidence-based, not calibrated, and explains the tool abstains with applicable: false on non-prose documents. Includes honest scope and limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured paragraphs with no superfluous information. First sentence states purpose, then input, output, scope, and alternatives. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers input formats, output fields (probability, lean, tells, reasoning, applicable), honest scope, and alternative tools. With an output schema existing, the brief return description suffices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Description explains the three parameters: text for inline content, bytes_b64 for base64 files, and filename for routing. Given 0% schema coverage, this adds meaningful context, though could include more constraints like size limits.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool estimates the probability that a document's text is AI-generated, distinguishing it from siblings like verify_document and verify_references by specifying scope and limitations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides when to use (prose) and when not to (forms/tables/numerics), and suggests alternatives (verify_document for authenticity, verify_references for citations). Also warns about false-positives for templated/coached/non-native writing.
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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