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?
No annotations exist, so description carries full burden. It discloses that probability is model confidence, not calibrated truth, and warns of false positives for templated/coached or non-native English writing. Also explains it only works on prose and returns applicable: false otherwise.
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?
Well-organized and front-loaded with purpose. Some slight verbosity but every sentence adds value. Could be trimmed slightly, but remains clear and efficient.
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?
Given output schema exists, description doesn't need to detail return values, but it does mention the fields. Covers input methods, scope, limitations, and alternatives comprehensively. Complete for this tool's complexity.
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?
Schema description coverage is 0%, so description provides all parameter meaning. It explains the three input methods (text, url, bytes_b64), mentions filename routing, and adds context (e.g., url is 'cheapest call'). Fully compensates for lack of schema descriptions.
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's purpose: estimating the probability that document text is AI-generated. It specifies the resource (prose documents) and the verb (estimate probability), and distinguishes from siblings by mentioning alternatives like verify_document and verify_references.
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 tells when to use (when someone asks if text is AI-generated) and when not to (for forms/tables/numeric documents), with clear examples. Also suggests alternative tools for non-prose cases.
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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