Skip to main content
Glama
206,568 tools. Last updated 2026-06-17 14:27

"namespace:io.github.Wxt-ai" matching MCP tools:

  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
    Connector
  • Generate an AI image using Avocado AI. Returns a jobId immediately; image generation completes in 10-60 seconds. After calling, use the check_job tool with the returned jobId to retrieve the result, once complete, check_job returns the image inline so it renders directly in chat. Run models_list to see available models. Costs 1-4 credits per image depending on model and quality.
    Connector
  • 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.
    Connector
  • Generate an AI image and place it directly on a user's Avocado AI storyboard. Drops 'Generating...' placeholder(s) on the board immediately, then the webhook swaps each placeholder for the final image when generation completes (10-60s). Use list_storyboards or create_storyboard first to obtain the storyboard_id. If the user has the storyboard tab open, they may need to refresh once for the image to appear (the canvas does not yet support live realtime updates from MCP). Costs match generate_image (1-4 credits per image depending on model and quality).
    Connector
  • Top AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by AI flag score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_flag_score: minimum AI flag score (default 8, min 5, max 10) 8 = notable · 9 = high-impact · 10 = exceptional - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_flag_score (0-10), ai_summary (full text), ai_confidence (0-10) Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
    Connector
  • Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Access the GitHub API, enabling file operations, repository management, search functionality, and…

  • Deepfake detection, media intelligence, and invisible watermarking for audio, image, and video via the Resemble AI API, plus docs tools. Remote MCP server (Streamable HTTP) — also published in the official MCP registry as io.github.resemble-ai/resemble-mcp.

  • AI-analysed news for a stock, newest first. Only returns articles processed by our AI pipeline (sentiment, flag score, summary). - days: look-back window in days (default 30, max 30) - limit: max articles returned (default 10, max 10) - status: "ok" = articles returned | "empty" = no news in window - Per article: title, published_at, ai_sentiment, ai_flag_score (0-10), ai_summary (full text), ai_confidence (0-10) Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
    Connector
  • Get the latest curated crypto news headlines. Returns real-time news items with headline, sentiment, categories, and sources. Use the category parameter to filter by topic (e.g. 'bitcoin', 'defi', 'ai'). Call get_categories first to see all available category codes. Args: category: Filter by category code (e.g. 'bitcoin', 'ethereum', 'defi', 'ai'). Omit to get news across all categories. limit: Number of items to return (1-10, default 5).
    Connector
  • Returns an honest comparison of how different validation approaches work - generic AI assistants, trend aggregators, passive scoring tools, and Demand Discovery AI - and where each one stops. Use when a user is evaluating approaches, asking "what makes Demand Discovery different?", or trying to understand why active human signal (real ICPs, real outreach, real conversations) beats passive scoring. Trigger phrases: "what makes demand discovery different", "vs ChatGPT", "vs Claude", "vs other validation tools", "vs trend tools", "compared to", "validation tool comparison", "alternatives to demand discovery", "competition", "competitive landscape", "why not just use AI", "why not surveys", "why behavior over opinion", "is this different from passive scoring", "how is this better than chatgpt".
    Connector
  • Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.
    Connector
  • Top AI-flagged news across all tracked stocks — the market-wide news briefing. Unlike get_stock_news (per-symbol), this scans the entire universe and returns the most notable articles ranked by AI flag score, newest first within each score tier. Use this for: - Morning briefing: "what happened in the market this week?" - Catalyst scanning: "what news is driving moves right now?" - Event monitoring: "which stocks have high-impact news today?" - min_flag_score: minimum AI flag score (default 8, min 5, max 10) 8 = notable · 9 = high-impact · 10 = exceptional - days: look-back window in days (default 3, max 10) - limit: max articles returned (default 10, max 25) - Per article: symbol, title, published_at, ai_sentiment, ai_flag_score (0-10), ai_summary (full text), ai_confidence (0-10) Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
    Connector
  • Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.
    Connector
  • Confirm an AI call after reviewing push-back questions, optionally providing answers to missing info. Required when ai_call returns state='pending_confirm'. Uses the original payment — no new payment needed. Returns call_id for polling with check_job_status(jobType='ai-call').
    Connector
  • Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.
    Connector
  • AI-powered ATS scoring with detailed section-by-section feedback, gap analysis, requirement mapping, and keyword strategy. Provide a job_description to score against a specific posting, or omit it for a general ATS readiness score. Requires authentication -- sign in at https://aiapplyd.com first. Free alternative: use score_resume for keyword-based scoring.
    Connector
  • AI-screened stock ideas actively flagged by the Stocklake pipeline. These are stocks the pipeline's AI agents have identified as worth attention — sourced from news analysis, sector screening, and sentiment signals. Parameters: - direction: "LONG" | "SHORT" | "BOTH" (default: all) - min_conviction: minimum conviction score 0-10 (default 7) - min_flag_score: minimum flag score 0-10 (default 8; 9+ = high conviction) - source: filter by signal source — "news" | "screener" | "sentiment" (default: all) - limit: max results to return (default 25, max 50) Returns: - count: number of ideas returned - ideas[]: each with symbol, direction, conviction (0-10), confidence (0-10), flag_score (0-10), source, rationale, expires - Note: ideas expire daily — active ideas represent the pipeline's current view. Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.
    Connector
  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
    Connector
  • AI-assessed sector intelligence: signal, cycle stage, rotation signal, drivers, alerts, and computed statistics per sector (RSI distribution, breadth, performance 1W/1M, top/bottom movers, historical percentiles). Pass a sector name for a single sector, or omit the parameter (or pass None) to get the latest assessment for all 11 sectors. Refreshed every ~4 hours by the market intelligence pipeline. Available to pro tier only (AI pipeline costs). For informational purposes only. Not financial advice.
    Connector
  • Get the summary and URL of a specific SearchShop AI Research Note by its slug (returned by search_research_notes).
    Connector