"A scheduling engine powered by AI" matching MCP tools:

• analyze_company

  Nordic Financial MCP

  AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'

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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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• 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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• 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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• 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. 67+ 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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• interpret_weibull_shape

  ReliaStats

  Given a Weibull shape parameter β (and optionally the characteristic-life parameter η), return a plain-language interpretation: which bathtub-curve regime β implies (infant mortality / random / wearout), what action that suggests (process-of-care / steady-state monitoring / maintenance scheduling), and — if η provided — closed-form MTTF and B-life numbers from the Weibull formulas. Pure-math + lookup, no engine call, fully deterministic. Use when a user reports a fitted β and wants to know what to DO with it. ANTI-FABRICATION: MTTF and B-life are exact closed-form values from the two-parameter Weibull (η · Γ(1+1/β) and η · (-ln(1-p))^(1/β)). Quote them verbatim.

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• list_services

  Parallax Ventures

  List Parallax’s services with real pricing. Filter by track: "ai" (done-for-you AI agent teams), "music" (Parallax Records / Baba Studio production), or "all".

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• recommend_framework

  jeda-ai

  Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }

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• get_portugal_holidays

  MCP Europe Tools

  Returns all Portuguese national public holidays for a given year as a structured list. Each holiday includes { date: 'YYYY-MM-DD', name: string, name_en: string }. Returns 10 mandatory national holidays defined by Portuguese law. Use when calculating business deadlines, delivery dates, payment due dates, SLA periods, or scheduling tasks that must avoid non-working days in Portugal. Does not include municipal or regional holidays (e.g. Lisbon June 13, Porto June 24) which vary by city.

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• get_research_note

  La Luer — AI Skincare Commerce

  Get the summary and URL of a specific SearchShop AI Research Note by its slug (returned by search_research_notes).

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• socrata_list_portals

  socrata-mcp-server

  List known Socrata-powered government open-data portals with their domain, organization name, and dataset count. Backed by the Discovery API domains catalog. Filtering is client-side substring match on the query parameter. Use this first when you do not know which portal to target, then pass the domain to socrata_find_datasets.

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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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• audit_brand_visibility

  Ninar AI

  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.

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• assess_weather_risk

  ParaSure

  [$0.10 USDC/call · Solana USDC · x402] Entry point for every agent flow. Given a business location and type, returns a weather risk score (0-1), the top perils ranked by severity, historical frequency data, and an overall risk level (low/moderate/high/severe). Powered by 5 years of Open-Meteo historical data — returns real data, not sandbox. Always call this first before requesting a quote.

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• get_engine_versions

  olympus-bets-analytics

  Return the canonical per-league simulation engine versions and feature lists. Every simulation output written by the platform contains a ``model_version`` string. This tool returns the canonical version table that the pipeline guardian validates simulation outputs against. Args: league: Optional league filter (e.g. "NBA"). Omit to return all leagues. Returns: ``{count, engines: [{league, engine, version, key_features, ...}]}``

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• searchCollaborators

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".

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• candidate_screening_ranking

  gapup-mcp

  AI-powered candidate screening and ranking for recruiters, hiring managers, ATS providers and recruitment AI agents. Ingests a job description and 1-50 candidate resumes, returning a ranked shortlist with score breakdowns across five weighted criteria: skills_match (tech stack and soft skills extracted from JD vs resume), experience_match (years vs seniority level inferred from JD), education_match (degree level + top-school detection), role_progression (Junior to Senior to Lead patterns), culture_fit_estimate (remote/hybrid, startup vs enterprise). Per candidate: overall_score 0-100, matched/missing skills, red_flags (job hopping, employment gaps, seniority mismatch), green_flags (long tenure, promotions), 3-5 interview questions, fit_summary. Diversity signals are first-name proxies ONLY with mandatory ethical WARNING. All processing is local -- no external API calls, instant response, privacy-preserving.

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• x711_substrate_daily_digest

  x711

  Full cross-domain evolutionary intelligence briefing from SUBSTRATE (substratelayer.com). Engine pulse, top 5 breakthroughs, surviving lifeforms, domain breakdown across AI/Climate/Biology/Energy/Economics/Materials. Cached 1hr. $0.10. Requires API key.

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• score_resume_ai

  AI Applyd

  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.

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• data.availability

  CodeReclaimers Capability Oracle

  Returns current availability status for Alan McIntyre (CodeReclaimers LLC). Use for fast scheduling checks before a full capability query.

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