A
licenseA
qualityB
maintenanceDeterministic fluff detector for AI-generated prose. No model, no API key.
3
1
MIT
Stipple — AI Text Detection
by sh.stipple
Server Details
Is this prose AI-written? A probability with the tells behind it. Free to start, no key.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- Sketchjar/stipple-mcp
- GitHub Stars
- 0
- Server Listing
- stipple-mcp
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Tool Definition Quality
A4.9/5.0
Tool DescriptionsA
Average 5/5 across 1 of 1 tools scored.
Server CoherenceA
Disambiguation5/5
With only one tool exposed, there is no possibility of overlap or misselection. The single tool's purpose is sharply scoped to detecting AI-generated prose.
Naming Consistency5/5
The sole tool name `detect_ai_text` follows a clear verb_noun convention and accurately communicates its function. There are no mixed naming conventions in a one-tool set.
Tool Count3/5
One tool is at the low end of the ideal range and feels thin compared to the typical 3-15 tool scope, though it fits the narrow server purpose. It is not an extreme mismatch because the tool is nontrivial and directly matches the server name.
Completeness5/5
The tool accepts text, URL, and base64 file inputs and explicitly abstains for non-prose documents, covering the main detection workflows for an AI-text-detection service. References to other verify tools are outside this server's stated domain, so no core capability is missing.
Available Tools
1 tooldetect_ai_textDetect AI-written proseARead-onlyInspect
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
A5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses that the probability is the model's confidence, not calibrated truth, that false-flagging can occur, that non-prose documents return applicable: false and abstain, and that URL fetching is server-side. This is substantial behavioral context that annotations alone do not provide.
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 well-structured with clear sections: core definition, usage trigger, input modes, return shape, and honest limitations. Every sentence adds actionable information, and the key scoping and usage details 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?
The description covers input modalities, expected output fields, abstention behavior, limitations, and alternative tools for out-of-scope inputs. Despite the presence of an output schema, it still summarizes the return object, and no critical calling context is missing.
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?
With 0% schema description coverage, the description fully compensates by explaining each input mode: text for pasted prose, url for server-fetched public links/PDFs, and bytes_b64 plus filename for base64 files. It also clarifies that exactly one document input should be provided, which is essential for correct invocation.
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 opens with a precise verb and resource: 'Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose).' It clearly defines the tool's scope and differentiates it from related tools by naming verify_document and verify_references for non-prose cases.
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?
Explicit 'USE THIS WHEN' guidance covers the exact scenario: someone shares prose and asks whether it was AI-written. It also provides explicit exclusion criteria (forms, tables, numeric documents) and names alternative tools for those cases, making selection unambiguous.
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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