"AI Generated Text Detection Tools" matching MCP tools:

• analyze_business_vertical

  agent-ready-kit

  Use this immediately after scan_site to give the user a 'what this means for my business' framing. Detects the site's business vertical (auto dealership, law firm, healthcare, home services, ecommerce, digital agency, etc.) from JSON-LD schema + scraped text. Returns expected AI-search lift %, current competitor adoption %, and a positioning pitch tailored to the vertical. **If `should_ask_user` is true, the detection is low-confidence — ASK THE USER what category their business is in before continuing, rather than acting on the guessed vertical.** Also returns the site title and meta description so the calling agent can render a Site Summary card.

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• detect_ai_text

  OpenWarrant — AI Text Detection

  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.

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• get_coverage

  BioCosm

  Report BioCosm's actual data coverage so you never mistake missing data for a real-world zero. An empty or absent field on a node means "not in BioCosm's data," never a true zero. BioCosm is AI-generated and may contain errors.

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• get_morning_brief

  Valuein — SEC EDGAR Fundamentals & Smart-Money Data

  Read the caller's Morning Brief — a daily AI-generated market digest covering overnight moves across the customer's own watchlists and theses, produced by the Workspace. Omit `day` to get the most recent brief available (not necessarily today's); pass a specific `day` (YYYY-MM-DD) to fetch that day's brief. It is normal for no brief to exist yet if the customer hasn't set up or recently generated one — that returns `found: false`, not an error. Tier: sp500+ (sample rejected).

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• agent_verify

  Colour Memory

  Verify that an AI-generated image actually used the colours specified in an agent_brief call. Supply the generated image (URL or base64) and the target palette from agent_brief colour_tokens. Returns a fidelity score 0-100, dE2000 distance per colour, match quality per colour (accurate/acceptable/drifted/ignored), and an overall verdict. Use after agent_brief + image generation to close the colour loop.

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• detect_ai_text

  OpenWarrant — Document Verification Suite

  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.

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• analyst_daily_summary

  whale-insights

  Get today's AI-generated commentary for all 10 analysts — 2-sentence summaries with signal count, confluence score, BTC trend (generated 9:00 UTC) — Returns today's AI-generated daily commentary for all 10 analyst personas. Summaries are generated each morning at 9:00 UTC using gpt-4o-mini based on the previous 24h of signals, Fear & Greed score, BTC trend, and cross-analyst confluence. Public endpoint — no authentication required. Only shortSummary is returned (2 sentences). Full commentary is available via the authenticated Pro endpoint /api/analysts/:id/daily-summary. Fields per analyst: analystId, analystName, summaryDate (YYYY-MM-DD), shortSummary, signalCount (signals in the past 24h), confluenceScore (0-100: % of other analysts with overlapping tokens in last 2h), fearGreedScore (0-100), btcTrend ('up'|'down'|'sideways'|null). Cached 30 minutes. Returns empty summaries array before 9:00 UTC on any given day. 60 req/min rate limit.

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• malware_get_template

  website-search

  Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.

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• vest_search_tools

  vest-mcp

  Search and browse AI tools available in Vest's cashback catalog. Returns names, slugs, categories, and live cashback rates. Use when the user asks what tools are available, wants to compare options, or needs a slug for vest_get_signup_link. Real triggers: 'what AI writing tools does Vest have?', 'show me coding tools with high cashback', 'find tools under $50/mo'. Do NOT use when the user describes a goal or mission — use vest_build_stack instead. Do NOT use to get a signup link — use vest_get_signup_link.

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• generate_and_attach_image

  Caulo

  Generate a NEW image from a text prompt via the platform's allowlisted image-gen provider (currently OpenAI gpt-image-1) and return an asset_id ready to attach to create_post / add_comment / send_dm. Requires the separate `media_authored` scope — granting `post` alone does NOT permit AI image generation. The user must have ticked the box on caulo.ai/settings/agents. Pipeline: caulo.ai's /api/media/generate calls the provider server-side, gets PNG bytes, runs them through the SAME /sign + /finalize Tier 0 / Tier 1 / Tier 2 moderation pipeline that protects human uploads (EXIF strip, polyglot neutralization, perceptual-hash kNN, Haiku Vision for CSAM / NSFW / rule violations). A rejected generation is the moderation pipeline doing its job — relay the reason to the user; reword the prompt if you retry. Provenance: every asset created via this tool carries `provenance='agent_authored'` and `generator_model='gpt-image-1'`. The image itself carries a persistent AI-generated marker — surfaced in the data (`media_ai_generated[]`) and rendered as a chip ON the image everywhere it shows. This does NOT change the post's text badge: a human-written post you illustrate with a generated image stays `human`, the image is marked AI. The two axes are independent — text authorship vs synthetic media (db/64). C2PA cryptographic preservation is NOT yet implemented (see SESSION_HANDOFF §10 backlog). Returns { asset_id, status: 'approved' | 'rejected', nsfw_level?, generator_model }.

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• edit_post

  Caulo

  Edit one of my own posts — text, media, or both. Media (optional): pass `media` to REPLACE the post's entire attachment set (same rules as create_post — each asset approved + owned + kind in {image, video}; up to 4 images OR 1 video, never both). Pass a new array to swap the images/video, pass `[]` to strip all media, or OMIT `media` to leave the current attachments untouched (text-only edit). Get asset_ids from `get_my_media` or `upload_media_from_path`. Attaching an AI-generated image does NOT change the post's text badge (db/64) — the image carries its own AI-generated marker. Returns the post's public media URLs (`media_urls`) so they can be displayed inline.

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• brave_summarizer

  brave

  Retrieves AI-generated summaries of web search results. Two-step flow: first call `brave_web_search` with `summary=true` to obtain `summarizer.key`, then pass it here. Pro AI tier required.

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• index_video

  Reka

  Index a video for search, QA, or full analysis. Processes the video through a pipeline of AI features. Typically takes 3-7 minutes; longer for long videos or the 'full' pipeline. Times out after 10 minutes by default. Pipelines: - search_only: transcription + captions + embeddings (enables search_videos) - qa_only: transcription + captions (enables ask_video) - full: transcription + captions + embeddings (enables all tools) Scene detection is enabled by default and produces scene boundaries for get_scenes. Pass scene_detection=False to skip it. Prerequisites: if using video_id, the video must be in 'uploaded' status. Use get_video to check status before calling this tool.

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• aidefense_cross_map

  website-search

  Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.

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• get_categories

  Gloria AI

  List all available news categories with their recap timeframes. Returns category codes that can be used with get_latest_news, get_news_recap, and other tools. Each category includes its code, display name, and how frequently recaps are generated.

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• scan_site

  AEO Scanner

  Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 77+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.

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• pathrule_take_snapshot

  Pathrule

  Record a point-in-time inventory of the user's project under a workspace. Remote MCP cannot see the filesystem, so YOU (the AI) collect this inventory with your own Read/Glob/Grep tools before calling this. Persist it so future setup, bootstrap, drift detection, and onboarding flows have structured evidence to reason over. Required: workspace_id. Strongly recommended: project_name, file_count, file_tree (cap at ~5000 entries — summarise deeper paths), file_extensions_summary, top_level_dirs, sampled_contents for README, package.json / pyproject.toml / Cargo.toml, CLAUDE.md, AGENTS.md, main config files (truncate each to ~4KB). Optional: git_head / branch / git_log_summary if you can read them, ai_notes for free-form observations.

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• get_stock_ai_summary

  Stocklake — AI Stock Intelligence

  AI research summary for a stock — verdict, confidence, key points, and risk factors. Generated by the Stocklake AI pipeline (stock_ai_summary.py) on a nightly refresh cycle. Returns: - verdict: "bullish" | "bearish" | "neutral" - confidence: 0-10 (how confident the AI is in its assessment) - flag_score: 0-10 (how notable/interesting this stock is right now — 8+ = worth attention) - summary: 2-4 sentence AI narrative - key_points: list of 3-5 bullish/neutral observations - risks: list of 2-3 specific risk factors - price_at_generation: stock price when the summary was generated - generated_at: UTC timestamp of last generation Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• list_projects

  CoreModels

  List the user's CoreModels projects as [id,name,accessLevel] (see the response "format" field). Use a returned id as graphProjectId for other tools. Pass searchTerm to filter by name (case-insensitive substring). Set includePublicProjects=true to also include public projects. Set includeAISummary=true to also return each project's saved AI-generated summary and the time it was generated (4th and 5th elements). Paged: page is 1-based; increment page up to the returned totalPages to get all results.

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• detect_drift

  dynamic-feed

  DRIFT DETECTION — audit a batch of AI-stated facts against live ground-truth in one call. Pass `claims` (a list of statements an LLM produced) or `text` (a paragraph that's split into claims); each is fact-checked and graded — drifted / confirmed / unverifiable, with a severity — plus a summary (drifted_count, overall). Use it to self-audit an answer before sending it. Args: claims: list of statements to check. text: alternatively, a paragraph that gets split into claims. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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