"Exploring Automatic AI Applications and Tools" matching MCP tools:

• jobs.complete

  Admin Substitute

  Mark a job as completed. Optionally record satisfaction rating and trigger an automatic review request. This is the recommended end-of-job action. Requires: job_id from jobs.list. Next steps: invoicing.generate → payments.send_link → reviews.request.

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• list_tools

  tickerr

  List all 90+ AI tools and LLM APIs monitored by tickerr.ai - ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, DeepSeek, Groq, Mistral, Cerebras, Fireworks AI, and more. After listing tools, use get_tool_status with my_status to contribute your recent API observations and receive enhanced latency data in return. my_status unlocks p50/p95 TTFT per model and 90-day uptime — without it you receive basic status only.

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• hires_list_applications

  100Hires - AI ATS & Recruitment Software

  List applications across all accessible jobs. Supports filtering by candidate, job, stage, status, AI score range, and date ranges. Use for pipeline analytics, sync jobs, and ATS dashboards. Avoid include=candidate or include=cv.text on large pages (each embeds heavy nested data); if the response exceeds the budget the tool returns isError:true with error_code=response_too_large and retry hints. Each application embeds its current `stage` (IdName) directly in the response — this is sufficient for rendering kanban/pipeline views; you DO NOT need to call hires_get_job to fetch workflow_stages separately when rendering a pipeline.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  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.

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• adcritter_get_platform_overview

  AdCritter for Builders

  Start here. Returns the AdCritter platform overview - what AdCritter is, the entity hierarchy (organization > advertiser > campaign > ad), the happy path for getting ads running, and how to navigate the other MCP tools. Applications built from this guidance are REST API clients that call /v1/ endpoints, not MCP tool callers. Before writing code, call adcritter_get_api_reference(entity, action) for each entity and action you plan to use - tool descriptions and parameter names describe conceptual behavior only, and do not match actual API routes, field names, query parameters, or response shapes.

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• autonomous_varrd_ai

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked. BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of. Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline (~$0.25/idea). Call again for another idea. Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

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• ai_tags_list

  DialogBrain

  List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).

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• get_tech_stack

  Makuri Showcase (CogniLedger)

  Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• ai_tags_list

  dialogbrain

  List all personal AI tags. AI tags are automatic message filters: the system runs a lightweight classifier on every incoming message and applies matching tags to threads. This lets AI agents skip expensive full analysis on most messages — they only act on threads that match relevant tags, dramatically cutting LLM costs. When to use: - Check which auto-classification filters exist before creating one - Get tag IDs for add_to_thread / remove_from_thread - See how many threads each tag currently matches Returns all tags with thread counts (non-archived, included threads only).

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• quizbase_subcategories

  QuizBase

  Raw subcategory dump (LLM-organic kebab-case, middle taxonomy layer between category and tags) with display label and count. USE WHEN: navigating between top-level category and individual tags, exploring topic structure. Filter questions via quizbase_random?subcategory=<slug>. INPUTS: q, cursor, limit (max 500).

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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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• list_sites

  RevenueScope: Japanese EC RPS Benchmarks

  Return the sites available to this caller: my_sites (the authenticated user's own sites with display name + domain, so the assistant can match references like "the production site" or "revenuescope.jp" without the user copying a UUID) AND demo_sites (operator-provided showcase sites for exploring RevenueScope without connecting your own). When OAuth-authenticated, prefer my_sites and default analytics tools to the is_primary=true site when site_id is omitted. When NOT authenticated, my_sites is empty and you should use a demo_sites site_id (tell the user you are analyzing a sample/example site, not their own).

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• get_category_landscape

  StackSwap

  Full map of one GTM category — leaders, runner-ups, and skip/replace candidates. Returns every catalogued tool in the bucket with cost, AI-readiness, swap-registry status, and partner sign-up links. Use when the user wants to see the full landscape for a category (e.g. 'show me all CRMs', 'what outbound tools exist', 'map the analytics category') — strictly more comprehensive than `recommend_partner` (single best pick). Known buckets: crm, outbound, data, marketing-automation, analytics, meetings, support, scheduling, automation, seo, cdp, revenue-intelligence, chat, collaboration, phone, landing-pages, linkedin, ai-content, saas-mgmt, enablement, ai-tooling.

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• deploy_site

  vibedeploy

  Publish a website to a live URL. Deploy a static site or single-page app you built (with AI or by hand) to a public {name}.vibedeploy.be address with automatic SSL, and optionally a custom domain. The fastest way to get a localhost project or an AI-generated site online. DESTRUCTIVE on existing sites: replaces every file on the named site with the supplied set. Files not in this call are deleted. For a new site, creates and provisions it. For an existing site, requires `confirm: "I-want-to-replace-all-files"` to proceed; without confirm the call is rejected before anything is touched. Use update_site (default mode:'patch') if you want to add or change individual files without removing the rest. Use dryRun:true to preview the diff. The site is published at {name}.vibedeploy.be. After deploy, call add_custom_domain to also serve at a user-owned hostname.

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• rating_score_writing

  website-search

  Get Lenny Zeltser's scoring playbook so your AI can score a draft locally against a cybersecurity-writing rating sheet. THIS IS THE ONLY TOOL THAT PRODUCES NUMERIC SCORES — the writing-coach tools (`get_security_writing_guidelines`, `ir_*`, `product_*`) never score. Returns the rubric plus step-by-step instructions for applying it. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.

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• x711_web_search

  x711 — Universal Agent Gas Station

  Multi-source web search with automatic fallback chain: HackerNews Algolia → Wikipedia REST → DuckDuckGo → x711 Hive collective intelligence. Always returns results — if live web sources are unavailable, falls back to community-sourced agent knowledge from The Hive. Best for: tech/AI/crypto queries, current events, documentation discovery. Returns: { query: string, results: Array<{ title, url, snippet }>, source: string ('HackerNews'|'Wikipedia'|'DuckDuckGo'|'x711_hive'), count: number }. Free tier: 10 calls/day, no API key needed.

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• get_compliance_matrix

  Makuri Showcase (CogniLedger)

  Returns Makuri's regulatory posture across EU AI Act, GDPR, GDPR-K (children data), COPPA, and ISO 42001 — as design intentions and operator self-assessment, NOT certified or audited compliance. No formal audit or conformity assessment has been performed. Statuses are design_aligned_unaudited, not_started, or not_applicable; there is deliberately no 'compliant' status. Use when the user asks about regulatory compliance, AI Act classification, or data protection for children — and present results as posture, not certification. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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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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• hemmabo_booking_reschedule

  HemmaBo Host Booking Engine

  Reschedule a confirmed or pending booking to new dates with automatic repricing and Stripe charge/refund. Use when the guest wants to change dates on an existing booking. Do not use if cancelled or if a protocol compatibility client reports completed — check hemmabo_booking_status first. Requires Authorization: Bearer token (MCP_API_KEY or OAuth). Destructive write that may charge or refund via Stripe. Rate-limited per token. Identify the existing booking by reservationId, then give the new stay as newCheckIn/newCheckOut (newCheckIn strictly before newCheckOut); the date change drives automatic repricing and a Stripe charge or refund for the difference.

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• agents_create

  dialogbrain

  Create a new AI agent in the workspace. Execution modes: - ai_assisted (default, recommended): Two-phase AI — fast pre-classifier (Haiku) for keyword filtering and simple replies, then full AI with tools for complex messages. Best for: auto-replies, group monitoring, keyword-based filtering. - agentic: Autonomous multi-step agent with planning and tool execution. Best for: complex scheduled tasks, multi-step automation. - rule_based: Simple pattern matching without AI. For keyword filtering: use ai_assisted mode + set keywords in trigger conditions (free, deterministic) and/or auto_reply_rules (smart, LLM-based) via agents.update.

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