"A study on skin aging and its factors" matching MCP tools:

• run_mmm

  mcp-server

  Re-runs a Marketing Mix Modeling study previously configured with setup_mmm. **Important:** Do NOT call this right after setup_mmm. The first run is automatically triggered by setup_mmm. Use run_mmm only to re-launch an existing study later (e.g., after data refresh or parameter changes). **Prerequisite:** Must have called setup_mmm first to obtain an account_id. **Duration:** The Meridian fit (MCMC) takes approximately 10-30 minutes depending on data volume. The user will receive an email when results are ready. **Results:** Results are written to the project's data warehouse (mmm_channel_summary and mmm_weekly_contributions tables). They can then be queried via execute_query.

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• openaq_find_locations

  openaq-mcp-server

  Find air-quality monitoring stations (measured by physical sensors, not modeled) near a point, within a bounding box, or by country. Returns each station's id, name, coordinates, distance from the query point (when searching by coordinates), country, provider, the parameters its sensors measure, and the timestamp of its most recent data (datetimeLast). Required first step: openaq_get_readings and openaq_get_measurements key on the location id this returns. Coverage is uneven and real — a station only reports the parameters it measures, and the absence of a nearby station means no monitoring there, not clean air. For dense modeled coverage anywhere on Earth, use open-meteo-mcp-server's air-quality tool instead.

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• firmaradar_start_aml_report

  Firmaradar

  Start generating an AML risk report ASYNCHRONOUSLY for a Norwegian company. Returns immediately with a report_id and status 'pending' — the report is built in the background. Poll `get_aml_report` with the report_id until status is 'done' (then read score/level/factors) or 'failed'. Use this instead of `get_aml_score` for large/complex ownership structures that may otherwise time out, or to start many screenings in parallel. Generates an auditable report stored for 60 months per Hvitvaskingsloven §35.

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• cancel_checkout

  Custom Blinds Shop

  Abort an unpaid checkout and release its checkout_id. Use if the customer changes their mind after create_checkout but before completing payment. Has no effect on orders already confirmed by webhook. To restart, create a new cart and checkout from scratch.

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• atlas_case_study_lookup

  ContrastAPI

  Look up a MITRE ATLAS case study — a documented real-world AI/ML attack incident. Each case study links a sequence of ATLAS techniques (techniques_used) to the incident. Default response is SLIM (description truncated to 240 chars); pass include='full' for the verbose narrative. Use this after atlas_technique_search to find which incidents have exercised a given technique. Drill into the full techniques_used array via bulk_atlas_technique_lookup in a single call (next_calls emits exactly that hint). Returns 404 when the id is not in the synced catalog. Free: 30/hr, Pro: 500/hr. Returns {case_study_id, name, description, techniques_used, next_calls}.

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• get_doc

  Uploadkit

  Fetch the full markdown content of a single UploadKit docs page by its path, formatted with title, description, source URL, and the body. When to use: after search_docs identifies a relevant page and you need its full contents to answer a deep question — prefer search_docs first, then get_doc on the top result. Reading the full page avoids relying on snippets that may omit critical context (callbacks, env vars, edge cases). Returns: a plain-text string — "# {title}\n\n> {description}\n\nSource: {url}\n\n---\n\n{content}". If the path is unknown, returns a not-found message suggesting list_docs. Read-only, idempotent.

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• open_document

  trident

  Opens a live Trident document and returns its full contents as Trident markup DSL — the human-readable text format used to author diagrams. Use this to READ and UNDERSTAND the diagram: its structure, labels, connections, and layout. Do NOT rely on this to enumerate entity IDs for programmatic use — the DSL can be very large and the output may be truncated. To get a complete, structured list of all entity IDs and counts, use get_document_summary instead. Requires a valid access token.

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• get_todays_projections

  olympus-bets-analytics

  Return today's free sports betting projections published by Olympus Bets Analytics. Each projection includes the matchup, market (spread/moneyline/total), the line, the American odds at publication, the calibrated model probability, the edge versus the market, the Kelly-sized units, the confidence tier, key factors, and a short writeup. These are PUBLIC projections — the same set published on https://app.olympus-bets.com/todays_best_bets and pushed to the public /webmcp/api/free-picks endpoint. Premium tier projections are not exposed here. Args: league: Optional league filter (e.g. "NBA", "NHL", "MLB", "CBB", "NFL", "SOCCER", "LOL", "GOLF"). Omit to return all leagues. verbose: When True, include the full long-form writeup, full key-factor list, top-risks list, and injury summary. Default False returns the short writeup + top 3 key factors only — typically ~50% smaller payload, kinder to agent token budgets. Set verbose=True when an agent specifically wants the detail (e.g., user asked "explain this pick"). Returns: ``{date, total, leagues_active, projections: [...]}``

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• search_slavonic

  slavonic

  Look an Old Church Slavonic word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content (singular, dual and plural), not invented. Any form of the word works; an inflected query is resolved to its lemma automatically via previously cached paradigms and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Old Church Slavonic equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).

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• search_norse

  norse

  Look an Old Norse word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content (including the verbs' mediopassive voice), not invented. Any form of the word works; an inflected query is resolved to its lemma automatically via previously cached paradigms and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Old Norse equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).

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• list_studies

  Edge Lab

  List every published (public) Edge Lab research study: slug, title, published date, and URL. Returns the authoritative catalog so the answer comes from the real index rather than a guess. Takes no arguments.

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• get_project_testimonials

  Kolmo Construction

  Get customer testimonials tied to a specific project (by slug or keyword) from the testimonials table. Returns star rating, customer name, project name, and quote text. Use to source social proof or case-study quotes for a particular job. For unfiltered reviews, use list_reviews.

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• predict_moment_quality

  Trillboards DOOH Advertising

  Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generative prediction 4. Always decomposes the prediction into causal factors WHEN TO USE: - Evaluating whether a creative will perform well in a specific context - A/B testing creative placement hypotheses before committing budget - Understanding which causal factors drive VAS for a creative - Comparing expected performance across different moment types RETURNS: - prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize } - causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1) - metadata: { creative_id, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How would a coffee ad perform at a transit station during morning rush?" predict_moment_quality({ moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range", creative_id: "coffee-brand-morning-30s" })

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• update_form

  socialloop-mcp

  Edit a form on a live event — rename and/or replace its questions. Pass questions with their ids (from list_forms) to keep responses mapped. Requires event_id + form_id; you must be a host.

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• twprojects-get_custom_item

  mcp

  Get a custom item type with its fields and sections inline, so you can see its schema before creating or updating records. Custom items are user-defined entity types — Contracts, Leads, Deals, or anything else a customer has set up on a project. Use these tools when the user refers to an entity that is NOT a built-in Teamwork concept (Task, Tasklist, Project, Milestone, Comment, Notebook, Company, Team, User, Tag). If you don't recognise an entity name in the user's request, assume it is a custom item and call twprojects-list_custom_items on the relevant project to confirm.

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• sec_report_search

  Drillr — The financial MCP for AI agents

  Use when you need narrative content from SEC filings — risk factors, MD&A, guidance language, deal terms, accounting policies, share structure. For consolidated financial numbers use run_sql on financial_statements instead. Semantic search over the full text of company-filed SEC filings; returns matching passages. Parameters: - query (required): natural-language search; phrase it as the concept or section name you want, e.g. "share repurchase authorization", "Risk Factors", "segment revenue". Run a few phrasings rather than one broad query. - ticker (required): the company whose filings to search. - filing_types (optional): array to restrict to specific types — 10-K (US annual), 10-Q (US quarterly), 8-K (US current/material events), 20-F (foreign annual), 6-K (foreign current), DEF 14A (proxy), S-1/F-1 (IPO), + amendments. OMIT to search all types — foreign issuers (e.g. BABA, TSM) file 20-F/6-K, so omitting avoids zero results. - period_start / period_end (optional): yyyy-mm window; set both to narrow to a date range, omit to search all history. - top_k (optional): max passages to return (default 5). Scope: indexes ONLY company-filed reports — NOT institutional filings (13F-HR/13D/13G; for those use insider_and_institution_activities with source='institution'). Filings carry narrative, not structured numbers — for revenue/margins/EPS use financial_statements first. Section targets (search the named section for the intent): non-GAAP / adjusted figures + reconciliations → earnings 8-K (Exhibit 99.1); dilution / SBC / buyback → "Shareholders' Equity" or "Capital Stock"; risk factors → "Risk Factors"; segment breakdown → "Segment Information"; management guidance → "Outlook" / "Guidance" in MD&A; exec comp / board → DEF 14A; accounting policies → "Critical Accounting Policies"; properties → "Properties".

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• clinicaltrials_get_field_values

  clinicaltrialsgov-mcp-server

  Discover valid values for ClinicalTrials.gov fields with study counts per value. Use to explore available filter options before building a search — e.g., valid OverallStatus, Phase, InterventionType, StudyType, or LeadSponsorClass values.

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• skincare_recommend

  La Luer — AI Skincare Commerce

  (Deprecated: use 'recommend' instead. Works identically.) Get a personalized La Luer product recommendation with ingredient-aware scoring, safety notes, and routine building. Use when the user wants advice on what to buy, needs help choosing between products, has a specific skin concern (acne, aging, dryness, sensitivity, etc.), wants a routine, or asks "what should I use for X." Do not use for browsing or listing products — use search_products instead. Returns scored products with explanations, usage instructions, and Shopify checkout. This tool analyzes ingredients, irritation risk, and product compatibility — use it over search_products when the user needs guidance, not just a product list.

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• describe_skin_types

  D Fetcher

  Returns the six Fitzpatrick skin types with sun-reactivity behavior and descriptive ancestry hints. Call this when the user's skin type is unknown so you can pick the closest match before calling calculate.

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• norse_search

  hub

  Look an Old Norse word up on Wiktionary and return its senses plus full declension/conjugation tables — attested content (including the verbs' mediopassive voice), not invented. Any form of the word works; an inflected query is resolved to its lemma automatically via previously cached paradigms and the result notes the resolution. With search_language='eng' the query is an English word instead: the result lists its per-sense Old Norse equivalents (the translations block) plus their expanded entries. Returns Markdown plus the same result as structuredContent matching the declared outputSchema. Results are cached server-side; first-time queries reach the live upstream politely and calls are rate limited — on a rate-limit error, wait a few seconds and retry. Content is from en.wiktionary.org (CC BY-SA 4.0 — attribute and share alike if republished).

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