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Signs of AI Writing

NUGET · SIGNSOFAI.MCP · SCANNED SEP 20

AI-writing detection (EN/ES) that shows the evidence: named tells, hidden characters, citations.

Available components

+1 this week 83 Trust /100
Trust breakdown (7 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →

Supply Chain Security100
  • No malware found by supply-chain analysis.Pass
  • No known CVEs affecting this package version or its production dependencies.Pass
  • No install/post-install scripts declared.Pass
  • No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency48
  • Source repository is publicly reachable at the declared URL. View diagnostics → Pass
  • Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
  • Clear OSI-approved license (MIT).Pass
  • Actively maintained (last published 3 days ago).Pass
  • Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability72
  • AI-judged instruction clarity (excellent).Pass
  • Context-footprint check failed: tool/resource definitions use about 2092 tokens (~209/item across 10 items; 10 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management90
  • Stability observed for 27 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Tool Safety100
  • No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
  • We read all 10 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
  • An AI judge read all 10 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Install

How do I install the Signs of AI Writing MCP server?

Signs of AI Writing runs locally as a NuGet package, launched with dnx SignsOfAI.Mcp@0.8.1 --yes. Ready-made configuration for Claude, Cursor, VS Code, Codex and 3 more is on this page, copied from each client's own documentation.

nuget · SignsOfAI.Mcp

# add to Claude Code
claude mcp add peopleworks-signs-of-ai -- dnx SignsOfAI.Mcp@0.8.1 --yes
// .cursor/mcp.json
{
  "mcpServers": {
    "peopleworks-signs-of-ai": {
      "command": "dnx",
      "args": [
        "SignsOfAI.Mcp@0.8.1",
        "--yes"
      ]
    }
  }
}
// .vscode/mcp.json
{
  "servers": {
    "peopleworks-signs-of-ai": {
      "command": "dnx",
      "args": [
        "SignsOfAI.Mcp@0.8.1",
        "--yes"
      ]
    }
  }
}
# add to Codex CLI
codex mcp add peopleworks-signs-of-ai -- dnx SignsOfAI.Mcp@0.8.1 --yes
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "peopleworks-signs-of-ai": {
      "type": "local",
      "command": [
        "dnx",
        "SignsOfAI.Mcp@0.8.1",
        "--yes"
      ],
      "enabled": true
    }
  }
}
# ~/.hermes/config.yaml
mcp_servers:
  peopleworks-signs-of-ai:
    command: "dnx"
    args: ["SignsOfAI.Mcp@0.8.1", "--yes"]
// ~/.netclaw/config/netclaw.json
{
  "McpServers": {
    "peopleworks-signs-of-ai": {
      "Transport": "stdio",
      "Command": "dnx",
      "Arguments": [
        "SignsOfAI.Mcp@0.8.1",
        "--yes"
      ]
    }
  }
}
// mcp.json
{
  "mcpServers": {
    "peopleworks-signs-of-ai": {
      "command": "dnx",
      "args": [
        "SignsOfAI.Mcp@0.8.1",
        "--yes"
      ]
    }
  }
}
Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 20 Sept 26 −2
    • Stability: pass → 0.90 functional
  • 19 Sept 26 +1
    • Stability: 0.97 → pass security
  • 17 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.

  • 16 Sept 26 0
    • Stability: 0.87 → unverified security
    • Tool safety: pass → unverified security
    • Capabilities: pass → unverified functional
    • Tool coverage: 100 → unverified functional
    • Package version: 0.8.0 → 0.8.1 functional
  • 15 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.

  • 13 Sept 26 −3
    • Stability: pass → 0.80 functional
  • 12 Sept 26 +1
    • Stability: 0.97 → pass security
  • 10 Sept 26 +1

    No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 20 Sept 2026 · Analysed nuget/SignsOfAI.Mcp@0.8.1

Provenance No attestation

The registry publishes no build provenance for this version, so there is nothing to verify.

Result No attestation
Ecosystem nuget
Reason Signature present, unreadable

Background: How many MCP packages publish verified provenance →

Dependencies 0 packages
Packages resolved 0
Tree resolution Complete

Background: SBOMs and build attestations, explained →

MCP tools · 10 exposed · ~2,092 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →

Tool Tokens
analyze_ai_writing ~160

Analyzes text for the stylometric tells of AI writing (English & Spanish): overused vocabulary, rhetorical crutches, syntactic tells, and low burstiness (uniform sentence rhythm). Returns an overall 0-100 "reads like AI" score, a plain-language verdict, per-category counts, document statistics, and a list of findings — each with the exact offending text, why it reads as AI, and an actionable fix. Runs fully offline; the text never leaves the machine. This is a signal, not proof of AI authorship.

NameTypeReqDescription
languagestringLanguage: "en", "es", or "auto" to detect. Default "auto".
textstringyesThe text to analyze.

No output schema declared.

No examples provided.

check_citations ~268

Compares a document against its own reference list and reports where the two disagree: a source cited in the text that appears nowhere in the bibliography, a number cited beyond the end of a numbered list, one DOI on two different works, a malformed DOI, a publication year that has not happened yet, a duplicated entry. Works for English and Spanish, numbered (IEEE/Vancouver) and author-year (APA/MLA) styles, and returns the line of every problem. Runs FULLY OFFLINE and looks nothing up: it cannot tell you whether a well-formed reference is a real paper, only whether the document contradicts itself. That is often enough, because an invented bibliography tends to fail against itself first. Nothing is sent anywhere. A missing reference is usually a slip rather than dishonesty, and it is always the writer's to explain — the correct response to a finding is to ask them for the source.

NameTypeReqDescription
currentYearinteger|nullWhat counts as the future, for the impossible-year check. Omit to use the current year.
languagestringLanguage for the wording of the messages: "en" or "es". Default "en".
textstringyesThe document, including its reference list.

No output schema declared.

No examples provided.

check_originality ~124

Compares two or more documents AGAINST EACH OTHER to find copied passages — a cohort of student submissions, a draft against its sources. For each document pair it returns the overlap percentage (case- and accent-insensitive) and the actual shared passages as evidence. This is NOT a whole-internet index like Turnitin; it only compares the documents you provide, fully offline. It surfaces evidence and lets a human judge — it never accuses.

NameTypeReqDescription
documentsarrayyesTwo or more documents to compare. Each has an optional title and its text.

No output schema declared.

No examples provided.

check_paraphrase ~144

Finds REWORDED copies between two texts — same meaning, different words, including across languages (e.g. English vs Spanish) — that a literal copy check can't see. It embeds each sentence and compares cosine similarity. NOTE: this SENDS BOTH TEXTS to the SignsOfAI server to embed them (endpoint from SIGNSOFAI_API_ENDPOINT). Requires the embedding feature to be enabled on the server.

NameTypeReqDescription
textAstringyesFirst document.
textBstringyesSecond document.
thresholdnumberCosine similarity threshold 0..1 to count as a paraphrase. Default 0.72.

No output schema declared.

No examples provided.

compare_to_baseline ~375

Compares one piece of writing against several earlier pieces by the SAME person, using function-word frequencies (Burrows's Delta). Returns how far the questioned text sits from that writer's centre, alongside how far each of the writer's own pieces sits from it — measured identically, so the scale is the writer's own variation rather than a threshold invented by this tool. Also returns which function words differ most, with rates per 1,000 words, and how many words are used at a rate the writer has never used them at. Runs fully offline; nothing is sent anywhere. WHAT THIS CANNOT DO: it cannot tell you who wrote something. There is no "different author" result and there must not be one in your summary either. Style moves with the assignment, the genre, the deadline, a co-author, an editor, and with a person simply getting better. A text outside the range is a reason to ask what changed; it is NEVER a conclusion, an accusation, or evidence of misconduct. The most valuable outcome is the reassuring one: a text INSIDE the range settles a suspicion, and saying so plainly is usually the most useful thing you can do with this tool. It refuses to answer on thin evidence and returns "Undetermined" instead of a number — do not work around that by rerunning with less text or by estimating one yourself.

NameTypeReqDescription
earlierWorkarrayyesEarlier pieces by the same writer. At least ~1,400 words in total across them.
languagestringLanguage: "en" or "es". Default "en".
questionedTextstringyesThe piece being asked about. At least 300 words.
questionedTitlestringOptional title for the questioned piece.

No output schema declared.

No examples provided.

extract_distinctive_phrases ~111

Extracts the most DISTINCTIVE phrases from a document — long, specific, proper-noun- or number-bearing wording most worth checking on the web — and returns each with ready-made exact-phrase search links (Google, Bing, DuckDuckGo). It does NOT search the web itself; it hands you the searches to run. Offline.

NameTypeReqDescription
maxPhrasesintegerMaximum phrases to return. Default 8.
textstringyesThe document text.

No output schema declared.

No examples provided.

inspect_characters ~233

Reports characters present in a text that typing does not produce: invisible/zero-width characters, letters borrowed from another alphabet to impersonate Latin ones (a Cyrillic "а" for an "a"), text direction controls, and hidden tag characters. Tools that rewrite text to defeat AI detectors insert these deliberately. Returns the exact codepoint, line and column of every occurrence, plus whether they are clustered (which ordinary copy-paste from a web page or a PDF produces) or spread through the whole document (which is what a rewriting tool leaves behind). Language-independent and fully offline. This is a checkable fact about a file, NOT proof of dishonesty and NOT a claim about who wrote the text: legitimate documents pick these up from PDFs, web pages and multilingual writing. The correct response to a finding is to ask the writer how the document was produced.

NameTypeReqDescription
languagestringLanguage for the wording of the messages: "en" or "es". Default "en".
textstringyesThe text to inspect, exactly as it arrived — not a cleaned copy.

No output schema declared.

No examples provided.

measure_predictability ~162

Measures how PREDICTABLE (generic) a language model finds the phrasing — its perplexity. Predictable, generic wording is common in AI writing, but formulaic human text scores predictable too and stylized AI can score varied: it is a signal, not proof. NOTE: unlike the offline tools, this SENDS THE TEXT to the SignsOfAI server to run the model (endpoint from SIGNSOFAI_API_ENDPOINT; defaults to the hosted API).

NameTypeReqDescription
languagestringLanguage: "en", "es", or "auto". Default "auto".
modelstring|nullOptional model id (see the server's model list). Empty = server default.
textstringyesThe text to score.

No output schema declared.

No examples provided.

search_catalog ~156

Searches the catalog of AI-writing "signs" the analyzer looks for (English & Spanish) — each with why it reads as AI and how to fix it. Useful as a reference / study aid, or to explain a finding in depth. Filter by keyword, language ("en"/"es"), and/or category (Lexical, Rhetorical, Syntactic, Statistical). Offline.

NameTypeReqDescription
categorystringCategory filter: Lexical, Rhetorical, Syntactic, Statistical, or empty.
languagestringLanguage filter: "en", "es", or empty for both.
querystringKeyword filter (matches title, examples, message, suggestion). Empty = all.

No output schema declared.

No examples provided.

write_report ~359

Produces the full analysis as a Markdown document a person can keep, forward to a writer, or take to an academic-integrity committee — the finished artefact rather than a summary to paraphrase. It contains the score, the signals that counted and the ones found at a rate people write at, the characters found in the file with their line and column, and the places where the document's citations disagree with its own bibliography. Checkable facts are named at the top and kept apart from the score, which is an opinion about prose. Every report prints how often this build is wrong, measured for the language actually analysed against texts written before 2022, and names the rules known to fire on human writing so the reader can weigh evidence that leans on one. Below the threshold that measurement supports, no verdict is given at all. Runs FULLY OFFLINE. The result contains material from the document, so treat it as you would the coursework itself: hand it to the person who asked, do not post it anywhere. Prefer this over paraphrasing the other tools' output when the user wants something to send, save, print or attach.

NameTypeReqDescription
documentNamestring|nullName of the file or assignment, printed on the report. Optional.
interfaceLanguagestring|nullReader-facing report language: "en" or "es". Independent from the analysed text. Defaults to English.
languagestring|null"en", "es", or "auto" to detect. Defaults to auto.
textstringyesThe document to analyse and describe.
titlestring|nullTitle for the document. Defaults to a title in the report language.

No output schema declared.

No examples provided.

Common questions

What is the Signs of AI Writing MCP server?

Signs of AI Writing is an MCP server listed in the public MCP registry as io.github.peopleworks/signs-of-ai. AI-writing detection (EN/ES) that shows the evidence: named tells, hidden characters, citations. This page covers its NuGet package (SignsOfAI.Mcp).

Is the Signs of AI Writing MCP server safe to use?

Signs of AI Writing scores 83 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

What tools does the Signs of AI Writing MCP server expose?

Signs of AI Writing exposes 10 tools: inspect_characters, measure_predictability, check_citations, analyze_ai_writing, write_report, and 5 more. Their descriptions and schemas cost roughly 2,092 tokens of context every time the server is loaded.

Is the Signs of AI Writing MCP server still maintained?

Signs of AI Writing is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

What licence is the Signs of AI Writing MCP server under?

Signs of AI Writing declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.