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

• list_investment_thesesA

  crashtestyourstrategyA

  Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.

  ConnectorNo auth

• query_knowledge_graphA

  Analytics Legends — SAP AI & Analytics MCP ServerA

  The RELATIONS between the platform's teaching objects — which Academy module teaches which concept, which study covers which module, what a concept relates to. THIS IS THE ONLY TOOL ON THIS SERVER THAT SERVES EDGES; the others serve rows. Ask it what connects to what, not what exists. SCOPE, AND IT IS NARROWER THAN 'the knowledge graph': it carries four node types — `concept`, `module`, `study`, `vendor` — and every edge whose BOTH endpoints are one of them. The whole graph holds twelve node types; the eight it does not carry are each either served by their own tool or named as not served at all, and `_meta.excluded_node_types` says which per type (consultant data is served at NO tier), so a missing type is a documented boundary and never a silent gap. Call it with `node_id` (e.g. `module:M178`, `concept:C001`, `study:ai-impact-2026-EN`) to walk one node's neighbourhood; with `node_type` and/or `query` to find a node id first. `edge_type` and `direction` narrow a walk. Read `_meta.available_edge_types` — computed from the served projection on every call — before assuming an edge type exists.

  ConnectorNo auth

• add_vocabulary_itemsA

  NextLangA

  Saves new words to the user's NextLang vocabulary for one language pair, so they join the spaced-repetition schedule. Use when the user asks to save or add words, for example from a text you discussed. Put each word in 'term' in the language being learned and its meaning in 'translation'; add a short example sentence when it helps. Words the user already has are skipped, not duplicated. Returns how many were added, already saved, or invalid. This is NextLang's own review list, quizzed through list_due_words and record_review, not a flashcard deck: when the user wants a deck to study in Anki, Quizlet, Mochi or Brainscape, use save_deck instead.

  ConnectorOAuth

• get_simulationA

  CrowdOSA

  Fetch the results of a study you ran, by id. Use it to read a study again, or to collect one whose tool call failed after the study launched: if a run tool returns error='timeout' or any error carrying recoverable: true, the study is still running (or has finished) and the error's sim_id names it. Do NOT re-run the tool — that launches and charges a second study. Wait 30-60 seconds, then call get_simulation(sim_id); poll again while status is 'running'. Returns the same shape as the run tool that started it, plus a top-level status (running | complete | failed | cancelled).

  ConnectorAPI key

• atlas_case_study_lookupA

  ContrastAPIA

  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}.

  ConnectorNo auth

• query_power_interconnection_queue_spp_v1A

  exascale.buildA

  Query the SPP generator interconnection queue — Southwest Power Pool's public GI Summary report, the waiting line of generation projects that have REQUESTED to connect to the SPP grid (the ~14-state central-US RTO: OK, KS, TX panhandle, NE, NM, MO, CO, ND, SD, AR, LA and more). Returns cited, project-level records with SPP's full published structure: the requested `capacity_mw` (SPP's headline `Capacity` figure) PLUS five other labeled MW columns SPP publishes — `max_summer_mw`, `max_winter_mw`, `requested_max_injection_mw`, `requested_nrd_mw`, `nameplate_capacity_mw` — served separately and NEVER blended; the `generation_type` (Wind / Solar / Battery/Storage / Thermal / Hybrid / Hydro — SPP encodes hybrids natively as `Hybrid`, so no is-hybrid is invented) and free-text `fuel_type` (slash-delimited combos like `Solar/Storage` kept whole); the study `current_cluster` (e.g. `DISIS-2024-001`, `Surplus`, `RTOE Transitional Cluster`) and regional `cluster_group` (`01 NORTH` … `05 SOUTHWEST`); the transmission owner `to_at_poi`; the `service_type` (`ER/NR`, `ER`, `NR`); the `substation_or_line`; and the lifecycle dates (`request_received`, `in_service_date`, `commercial_operation_date`, `date_withdrawn`). Group or filter by `native_status`, `generation_type`, `fuel_type`, `service_type`, `current_cluster`, `cluster_group`, `to_at_poi`, `county_fips`, or `state`; filter `request_received` / `commercial_operation_date` by their `_from` / `_to` ranges. Pass each parameter as a top-level key of `params` (flat — not nested). Example: `{"native_status": "DISIS STAGE", "generation_type": "Solar"}` for solar in the DISIS study stage; `{"native_status": "DISIS STAGE", "group_by": ["state"], "order_by": "capacity_mw", "top_n": 5}` for the active study pipeline's biggest states by requested MW. Returns JSON aggregates with citations and optional row-level records when `include_records` is true; every value carries `source`, `as_of`, and a `source_row` verifiable with get_source_evidence_v1. `capacity_mw` is REQUESTED capacity, not built: historically the large majority of queued megawatts withdraw before they are built — in the SPP file ~66% of rows are `WITHDRAWN`. NEVER read a queue-MW total as installed or operating capacity — it is additive across distinct rows but is a REQUESTED total only. Always scope by `native_status`, which is SPP's OWN status vocabulary served VERBATIM (`IA FULLY EXECUTED/ON SCHEDULE`, `IA FULLY EXECUTED/COMMERCIAL OPERATION`, `IA FULLY EXECUTED/ON SUSPENSION`, `IA PENDING`, `DISIS STAGE`, `FACILITY STUDY STAGE`, `SPECIAL STUDY`, `ERAS`, `TERMINATED`, `WITHDRAWN`, …); it is never mapped to a lifecycle enum and there is no derived active/withdrawn flag — that equivalence is the analyst's. For built/operating capacity use query_power_capacity_v1. SPP's GI Summary is regenerated on demand and SPP keeps no per-vintage archive, so this serves SPP's CURRENT queue (the response `as_of` is SPP's own "Last Updated On" stamp); it is NOT a deep point-in-time history — history accrues forward from first capture, so there is no `as_of` time-travel parameter. SPP only — never summed, deduped, or compared across ISOs. For the MISO interconnection queue use query_power_interconnection_queue_v1; for PJM use query_power_interconnection_queue_pjm_v1 (or query_power_interconnection_queue_pjm_cycle_v1 for PJM's cluster/cycle grid); for the CAISO (California) queue use query_power_interconnection_queue_caiso_v1; for the NYISO (New York) queue use query_power_interconnection_queue_nyiso_v1; for the ISO-NE (New England) queue use query_power_interconnection_queue_isone_v1; for the ERCOT (Texas) queue use query_power_interconnection_queue_ercot_v1. SPP's GI queue is GENERATION-only and infers no load/data-center type — that interpretation is the analyst's, from cited rows.

  ConnectorNo auth

• get_metricA

  PreReasonA

  Get a single metric with pre-reasoned signal interpretation, trend analysis (7d/30d/90d), confidence score with factor breakdown, momentum, and percentile ranking. Name the metric; list_metrics lists them and searches them by word. Needs a PreReason account: a signed in connection or an API key. Returns analyzed context (signal, trend direction, momentum, percentile among every stored daily close), not raw values. The signal is bullish, bearish or neutral on crypto series, and above-trend, below-trend or at-trend on every other metric, where the value sits against its trend, so a high yield reads above-trend. Use it to read where a metric stands against its trend and its own history, not just its number; for a reasoned view across several series, use get_context with a briefing. Every plan reads each metric's latest reading. Pro and above read every metric in full; on other plans, a metric outside the plan returns its latest reading only.

  ConnectorNo auth

• describe_servicesA

  Venture Insights Concept DiagnosticA

  Get Venture Insights' live service catalogue: the FREE Concept Diagnostic (a research-backed viability study of one venture concept, delivered to the founder's inbox) and the paid study tiers with live SAR prices. Call this first when your user asks what Venture Insights offers, what it costs, or whether the free diagnostic is worth requesting.

  ConnectorNo auth

• fluxink-mcp-chatgpt-create-layoutA

  fci-mcpA

  FluxInk document layout generator. Transform raw text content into a structured PDF using one of seven study or work templates, then preview it in an embedded PDF viewer widget. Supported layout_type values. cornell is the Cornell note taking layout with cue, notes, and summary. bullet_points is a clean bulleted summary. zettelkasten is atomic linked notes. journalism_5w1h is who, what, when, where, why, and how. meeting_add is a meeting agenda plus action items. sq3r is Survey, Question, Read, Recite, Review study notes. pso is Problem, Solution, Outcome. Use this when the user asks for a Cornell sheet, bulleted summary, Zettelkasten card, 5W1H breakdown, meeting agenda or minutes, SQ3R study sheet, or PSO writeup. Use this when the user wants to turn raw notes, lecture transcript, or source material into a printable PDF or formatted study sheet. Use this when the user asks for a downloadable PDF document of their content. Do NOT use this when the user just asks for a plain summary in chat. Give it inline. Do NOT use this when the user wants to handwrite or draw something. Call show_handwriting_canvas instead. Do NOT use this when the user wants text in a personal handwriting style. Call show_style_canvas instead. Do NOT use this for plain informational requests with no document generation intent. Always pass the source material verbatim in the content parameter. Do NOT pre summarize. The layout engine handles structuring. Pick the layout_type that best matches the stated purpose. If unclear, ask one short clarifying question instead of guessing. Do NOT re-call if a layout PDF is already visible from a previous turn unless the user explicitly asks for a different layout, different content, or a regeneration. After calling, write a single short acknowledgement and do NOT restate the PDF content.

  ConnectorNo auth

• get_freshness_summaryA

  DataPulseA

  Return a freshness-at-a-glance summary of the published catalogue: fresh, aging, stale, and reference counts plus the latest health check time. Use it for catalogue-level freshness context; do not use it to enumerate affected datasets—use find_stale instead. It reads the latest published snapshot, so missing counts or check time mean the artifact omitted them; DataPulse is read-only, requires no API key, and the edge limits clients to roughly one request per second with a small burst, so pace or retry.

  ConnectorNo auth

• get_study_planA

  Fiveable for AP StudentsA

  Reads one owned study plan, stable task ids, completion state and revision. A plan that is still being built reports pending with a retry delay, so poll this after create_study_plan rather than creating again. Use its revision for task changes. Reading older plans never changes them.

  ConnectorNo auth

• remove_layerA

  MorphaReelsA

  Delete a video, image, text, or shape LEAF layer. Errors on a group.<id> (dissolve it with ungroup_layers instead) and on a pinned layer such as the canvas backdrop. Deleting a video layer also takes everything welded to that clip: its welded audio overlay(s) and its welded caption lines, both of which derive their timing from the clip's trim and have no meaning without it.

  Connector

  Destructive

  No auth

• studies_get_evidenceA

  ZentrikC

  Use this when you need to review what a Study found: aggregate evidence, findings, citation references, freshness and next action. Standard connections also receive paginated individual rating feedback by default. Managed connections return aggregate findings by default; includeFeedback=true requires original-source permission and adds the feedback feed with participant identity. feedbackLimit and feedbackOffset page that feed. Use studies.get_responses for individual research answers; that is a separate original-source read.

  ConnectorOAuth

• load_skillA

  AeviaA

  How this Aevia connection works, plus playbooks for results, reports, systems. Returns a skill's playbook as the result; takes no action itself. A playbook is step-by-step guidance for a multi-step job, too long for a description. `connector_guide` covers how this connection works: workspaces, refs, finding data, the library, building, calculating and failures. `assessment_interpretation` covers reading and comparing results: what an indicator result is and is not, a figure's anchors, functional-unit equivalence and comparison discipline. `report_writing` covers writing results up as a document, from a screening note to an ISO 14044 study. `product_system_authoring` covers building a new product system with `lca_compose_assembly` / `lca_compose_linked`: build order, which of the two fits, failure modes, and tagging life-cycle phases. A loaded playbook stays in context; one load per session suffices. Skills are gated on what the token can call; an unavailable request returns the available list.

  ConnectorAPI key

• hourly_intensityA

  Emission FactorsA

  Get hourly grid carbon intensity (kg CO2e per kWh) for any US ZIP code, derived from EIA-930 hourly fuel-mix data. Data lags up to about a day (not real-time). Returns time series with per-hour fuel mix, total generation, and carbon intensity. For timing and analysis (demand response, load shifting, grid patterns), not inventory reporting: it uses fixed per-fuel combustion factors on in-BA generation (no imports), so its values don't match eGRID's annual factors. For live or forecast intensity, WattTime or Electricity Maps are better options.

  ConnectorNo auth

• prism_studies_deleteA

  ZeroWidth PrismB

  Permanently deletes a study with its responses, interviews, analysis, and share links. Right for a draft that won't be fielded or a duplicate; for a live study the user just wants to stop, prefer prism_studies_update closed: true — that keeps the data. Study ids come from prism_studies_list_workspace. May return `needs_confirmation` — name the study and its response count, then wait.

  Connector

  Destructive

  No auth

• prism_studies_deleteA

  ZeroWidthB

  Permanently deletes a study with its responses, interviews, analysis, and share links. Right for a draft that won't be fielded or a duplicate; for a live study the user just wants to stop, prefer prism_studies_update closed: true — that keeps the data. Study ids come from prism_studies_list_workspace. May return `needs_confirmation` — name the study and its response count, then wait.

  Connector

  Destructive

  No auth

• covadoe_design_powerA

  CovaSyn Chemistry ToolsC

  Planning-time statistical power from a design's REAL model matrix — no data needed. Builds the design and takes `ncp = effect_size² / (X'X)⁻¹_jj`, so it is correct for centre points, axial points and unbalanced designs — unlike `covadoe_power_analysis`, which assumes orthogonality and overstates power on a CCD, Box-Behnken or DSD. Use this when planning an experiment; use `covadoe_post_hoc_power` only once you have measured responses. `design_type` + `n_factors` builds the design (same generator as `covadoe_create_design`, so the answer describes the design you would actually get). Common aliases resolve ("full_factorial", "ccd", "dsd"). Alternatively pass `design`, an explicit coded run matrix, to score a design you already hold — D/I-optimal, mixture, split-plot or hand-built. `n_runs` / `model_terms` are forwarded to the generator for the optimal designs. `model`: "linear" | "interaction" | "quadratic" | "quadratic_no_interaction" (a DSD supports only the last). `term` names the coefficient — "x1", "x1^2", "x1*x2"; omit it to get power for every term and group, which differ sharply on the same design. `alpha` is the significance level; the CCD axial distance is `axial_alpha`, and the two are easy to confuse. CCD variant: `axial_alpha` defaults to ROTATABLE, (2**n_factors)**0.25 — the design JMP, Design-Expert and Minitab hand you. Pass a number, or "orthogonal" for the α where Σx² = n_runs (at 3 factors: main-effect power 0.9400 against the rotatable 0.8846 on the same 17 runs; the orthogonal α is also the premise of `covadoe_power_analysis`, so on that design the two agree for a main effect and disagree for every other term). `face` — "circumscribed" (default), "faced" (star points on the faces, α = 1) or "inscribed"; a bool works, and an unknown value is rejected rather than quietly treated as circumscribed. Returns power, power_by_term, power_by_group, df_error, relative_variance ((X'X)⁻¹_jj), the resolved design_summary (the α and factorial level actually built — read both, an inscribed CCD always reports α = 1), and `orthogonal_shortcut_power` — what `covadoe_power_analysis` would have claimed — with its signed error (signed: the shortcut does not always overstate). Returns `estimable: false` plus a reason when the model cannot be fitted on the design.

  ConnectorAPI key

• asterwise_get_western_daily_factorsA

  AsterwiseA

  Every Western factor for one person on one day, in one shape, with no weighting. Each factor carries its raw value and range, its frame, the verdict Lilly's rules give, the rule's source, signification ids, the time it holds and whether it depends on the birth time. WORKFLOW: BEFORE: asterwise_get_western_natal. AFTER: asterwise_get_western_events — the same over a range, as a timeline. INPUT CONTRACT: birth — WesternBirthData (house_system used for natal_house). target_date (optional YYYY-MM-DD; defaults to today in the birth timezone). target_time (optional HH:MM; defaults to 12:00) — the local time in target_timezone the day's sky is read. target_timezone (optional IANA). DO NOT CONFUSE WITH: asterwise_get_western_transits_daily — the aspects only, with no rules. asterwise_get_western_horoscope — AI-written sun-sign text. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-daily-factors/

  ConnectorOAuth

• asterwise_get_western_daily_factorsA

  AsterwiseA

  Every Western factor for one person on one day, in one shape, with no weighting. Each factor carries its raw value and range, its frame, the verdict Lilly's rules give, the rule's source, signification ids, the time it holds and whether it depends on the birth time. WORKFLOW: BEFORE: asterwise_get_western_natal. AFTER: asterwise_get_western_events — the same over a range, as a timeline. INPUT CONTRACT: birth — WesternBirthData (house_system used for natal_house). target_date (optional YYYY-MM-DD; defaults to today in the birth timezone). target_time (optional HH:MM; defaults to 12:00) — the local time in target_timezone the day's sky is read. target_timezone (optional IANA). DO NOT CONFUSE WITH: asterwise_get_western_transits_daily — the aspects only, with no rules. asterwise_get_western_horoscope — AI-written sun-sign text. Full output and error contract: https://docs.asterwise.com/mcp/tools/get-western-daily-factors/

  ConnectorOAuth

• prism_panels_runA

  ZeroWidth PrismB

  Starts a background run: every person on the panel answers the study (or, for kind interview, sits with the study's interviewer). Spends workspace credit — give the person the estimate from prism_panel_runs_estimate first. Works on draft studies. Answers land as their own result set, labeled synthetic, never counted as responses. Follow with prism_panel_runs_get. May return `needs_confirmation`.

  ConnectorNo auth