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784,297 tools. Updated 2026-11-15 08:24

"MEGA" matching MCP tools:

  • Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a `trending_scan` of the top ~200 markets by weekly volume; pass `event` for the strongest per-event partition_check, or `topic` for a themed cross-event scan. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FEES: every opportunities[] row and partition_check.arbitrage carry edge_pp_gross (== gap_pp / overround_pp), fees_pp, edge_pp_net, net_positive, plus polymarket_fee_pp, fee_basis and fee_categories[]. BOTH cost components are modeled: Polymarket's own per-category TAKER FEE (fee = shares × rate × p × (1-p), rates crypto 0.07 / sports-economics-culture-weather-other 0.05 / finance-politics-mentions-tech 0.04, geopolitics and world events fee-free; verified against Polymarket's own docs as of 2026-09-13) and Polygon gas (~$0.02/leg). The taker fee dominates: ~$1.75 per 100 shares on a crypto market at 50c versus $0.02 of gas, so rows that looked profitable before fleet #1927 may now show net_positive:false — that is the correction, not a regression. Each leg is priced at ITS OWN market's rate and price (the fee curve peaks at 50c and falls toward both extremes). fee_basis says where the rate came from: 'payload' (read off the market, the normal case), 'category' (mapped from its fee category), 'fee_free', or 'fallback' (rate unknown — charged at the modal 0.05 rather than assumed free, so an unreadable market is never reported as costless). Where fill_check reprices against live depth, this does NOT double-count that spread cost. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.
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  • Search live Vee3 agent tools by keyword or short task description. Call this first when you are unsure which tool to use. Returns ranked matches with tool_name, summary, and cost for each hit. Use meta-tools.describe on the best match for full request and response schemas. Optional group_id narrows results to one capability group. limit defaults to 8 (maximum 20). Cost = 0 tokens.
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  • Search Chinese apparel industrial clusters and textile markets. USE WHEN user asks: - "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated" - "what is the largest [silk / cashmere / down jacket] industrial cluster in China" - "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili" - "recommend an industrial cluster for sourcing [product]" - "where should I set up a sourcing office for [category]" - "list mega clusters for [category]" - "fabric markets in Zhejiang / Jiangsu" - "accessories / trim / zipper / button markets in China" - "which province dominates [category] exports" - "follow-up: 'tell me more about Humen's cluster scale'" - "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场" - "做 [品类] 应该去哪个产业带 / 集群推荐" Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more. Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks. WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view). RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] } EXAMPLES: • User: "Where are the biggest denim clusters in China?" → search_clusters({ specialization: "denim", scale: "mega" }) • User: "Show me fabric markets in Zhejiang" → search_clusters({ province: "Zhejiang", type: "fabric_market" }) • User: "童装产业带有哪些" → search_clusters({ specialization: "童装" }) ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province. • Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters." AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids. NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces. 中文:搜索中国服装产业带和面料市场。
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  • Preview, without writing, how one completed owned Accounting run's WRITEOff work plan would map to stable Meta Council tickets. Reports creates, updates, unchanged tickets, human-edited generated fields that will be preserved, and identity conflicts. Requires both accounting:read and tickets:read; tickets:write also satisfies the ticket-read grant. Preview before committing because tickets are not field-encrypted; work-plan text may reproduce source-derived snippets or parser details, and after commit tickets:read can read it without accounting:read. Check the item count before committing. Statement-derived runs currently plan one ticket per unclassified transaction line, so a several-hundred-row statement plans several hundred tickets. When the count is large, report it and confirm with the owner rather than committing a board-flooding sync.
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  • Update a draft or sent invoice's header fields (client, terms, due date, tax rate, notes). Only the fields you pass change. Blocked once paid/void — void and re-issue instead. invoice_id is required.
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  • Retire an owned ticket from the working set, or restore one. Archiving is a different axis from status: the ticket keeps whatever status it had, so a done ticket stays done and a blocked one comes back still blocked — never use status 'cancelled' to mean 'archived'. An archived ticket disappears from ticket_list, the stats, the buckets, the attention queue, and outbound JIRA/Linear sync, but keeps its id and URL so citations stay valid, and it refuses edits until restored. Cascades to the active subtree; restore brings back exactly what was archived alongside it. Nothing is destroyed — this is not a delete, and no delete tool is exposed.
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  • Description of my MCP server mega cool capabilities, lesgo

  • Analyze, edit and launch Meta (Facebook and Instagram) ads from Claude, ChatGPT, Cursor or Codex. Query insights and campaign health, change budgets, bids, status and targeting with undo, bulk-launch many ads across campaigns and ad sets from a live ad's settings, and duplicate by post to keep social proof. Choose per ad account what the agent may do and what waits in the Agent Inbox for approval. Requires a paid Ads Uploader account with MCP access.

  • Record that an owned ticket cannot complete until another owned ticket is done. The edge is never erased: correct a mistake with ticket_dependency_remove, which marks the edge removed and appends to a ledger rather than deleting anything. Re-adding the same prerequisite reinstates that original edge instead of creating a second one, so an edge removed and restored says so. An edge stops blocking when the prerequisite reaches done, and only then — archiving the prerequisite does not release it. Only epics are gated, so an edge on a task is recorded and reported but never blocks its completion. Rejections carry a stable code: dependency_self (422), dependency_duplicate, dependency_cycle, dependency_graph_too_large (409), ticket_not_found (404).
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  • Export the exact previewed owned ticket to Jira or Linear, using expected_export_hash from preview_ticket_export and the same destination. Requires explicit integrations:write, tickets:write, and independent human approval of the complete preview. Persists the operation before possible remote writes. After an uncertain response, inspect list_ticket_provider_effects and reconcile the original intent; never blindly export again. Remote confirmation is observed, not atomic protection against concurrent remote writers.
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  • Finds people by current company, title, country (ISO-2) or location and returns name, title, company and profile link, up to 25 results. Use to identify who to contact at a company. Work emails are not included: call apione_email_find for each person you want. Give at least one filter. Set count to cap spend. Not for details on one named person (use apione_people_enrich). Cost: 3 credits per result returned, so a full page of 25 results costs 75. No results, no charge.
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  • Prices Kalshi daily high-temperature markets against the NWS forecast for the market's OWN settlement station, and measures whether that forecast actually beats the market. Two modes. LIVE (default): returns the full strike ladder for one city and settlement date with market_prob (mid), forecast_prob, and edge_pp per strike, plus the settlement clause verbatim. BACKTEST (`backtest_days: N`): scores an archived gridded forecast against the market on settled days and returns brier_market vs brier_forecast with a plain-English `verdict`, so the edge is MEASURED rather than asserted. READ THE WARNINGS — they are not boilerplate. (1) These markets DO NOT settle on the NWS. They settle on The Weather Company (weather.com) at a Kalshi station code such as CLINYC, which the response quotes verbatim; so part of every edge_pp is NWS-vs-Weather-Company disagreement about the same day at the same station, which is not mispricing and not tradeable. `settlement_vs_forecast_basis_f` from backtest mode is that part as a number. (2) The station is DERIVED from the settlement clause, never from the city name: Chicago settles at MIDWAY and New York at CENTRAL PARK, so a city-centre forecast would misprice a whole ladder. A station that cannot be resolved yields rows with no forecast and a reason, never a guessed coordinate. (3) forecast_prob assumes a normal distribution around the NWS high whose width is ASSUMED, not fitted (stated in `distribution_assumption`) — run backtest mode to see whether it is calibrated. (4) edge_pp is gross: no Kalshi fees, no bid-ask. MEASURED RESULT, AND IT IS NOT THE FLATTERING ONE: on the first backtest (KXHIGHNY, 13 settled days to 2026-09-11, 58 market observations) the MARKET beat the forecast — Brier 0.1008 for the market against 0.1594 for the archived gridded forecast, lower being better. So on that sample there is NO forecast edge to sell, and a large edge_pp is more likely to be the model disagreeing with a better-informed market than an opportunity. The measured settlement-vs-forecast basis was 1.7F mean absolute over 8 pinnable days, slightly warm-biased, which is a big share of a typical edge_pp on a 2-degree bracket. Re-run backtest_days before believing any edge; if a later sample reverses this, the numbers say so. NWS is US-only, so the ~30 international Kalshi weather series (London, Paris, Tokyo) return market prices with forecast_unavailable rather than a forecast. Precipitation series are listed but not yet priced. Cities: nyc, chicago, los angeles, miami, austin, houston, denver, philadelphia — or pass `series_ticker` for any other (e.g. "KXHIGHTBOS").
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  • Claygent-style per-row research: answer ONE question about ONE company and return a JSON object matching your output_schema, with every field wrapped in its own evidence — {value, evidence_url, quote, status}. status is NEVER self-reported by the model: "found" means this code independently verified the model's quote is a VERBATIM substring of text actually fetched; "inferred" means a value was given but the quote could not be verified; "not_established" means no source answered the field, and its value is always null — never guessed, never a blended score. Sources: "web" fetches the company's own site (entity.domain required — homepage, /about, /leadership) via fetch_url; "catalog" checks entity_profile (SEC EDGAR; resolved:false for a private company is expected, not a failure); "both" (default) tries both. max_cost (USD, default 0.02, max 0.50) scales how many pages are fetched and refuses up front if it is too small to pay for even one extraction call (floor $0.002) — never silently overspends. A row where every source comes back empty costs nothing: every field is mechanically not_established with NO model call made. For a boolean/derived field (e.g. "does this company sell to healthcare"), the quote only has to SUPPORT the value — a descriptive sentence about customers/products counts, the source need not state the answer verbatim. leadership_page_status ("fetched"|"not_found"|"not_attempted", plus leadership_note) says plainly whether a leadership/team page was actually read, so a named-person field (head_of_sales) coming back not_established is distinguishable from "this company just doesn't publish one" vs. "we never checked". model_output_empty:true means the model returned nothing usable (a transport/budget problem) — every field is not_established for THAT reason, never read as 20 individual "no" judgments. Use with a fixed question + schema across many rows for "Claygent over N companies with a hard cost cap" (composes with batch callers). For picking from a fixed label list instead of extracting open fields, use classify_row (cheaper, one model call).
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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a `claim_token`; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • PERMANENTLY DELETES everything on a Muse account: every chat, file, document, web app, podcast, the agent's memory and its name. It cannot be undone. Use only when the user explicitly asks to reset the account. Two steps: call it without confirm first — nothing happens, and it returns the warning to show the user; then call it again with confirm = the account email ONLY after the user has read that warning and typed the email themselves. Never fill confirm in on your own. If the call errors or times out, do NOT call it again: the reset may have run — check with muse_get_account or muse_list_chats first. POST /accounts/{email}/reset.
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  • Enumerate the valid values for a parameter, straight from the backend. Call this BEFORE guessing a parameter value. `kind` selects which catalog; three kinds are scoped by a query parameter (`ticker`, or `q` / `cik` / `manager_type`) and refuse the call without it: - "event_types" -> valid `event_type` for `list_realtime_events` (eps_update, company_update, biggest_mover, ...) - "financial_items" -> standardized EDGAR line items ("revenue", "operatingIncome") for a CUSTOM report's `financial_items` (`generate_report_for_stock`, or a scheduled "create-full-report" step) - "financial_ratios" -> EDGAR ratios ("grossProfitMargin") for that report's `ratios` - "as_reported_items" -> NEEDS `ticker`. The filer's OWN tagged XBRL concepts from its 10-Q / 10-K filings — {concept, statement_type, label, standard_concept, latest_period, facts} — for a custom report's `as_reported_financial_items` - "revenue_segments" -> NEEDS `ticker`. The filer's revenue segments as tagged in its filings — {product_label, product_member, product_axis, concept, latest_period, facts} — for a custom report's `revenue_segment` (pass the product_label). A name lookup only: never sum members across it - "institutional_managers" -> NEEDS `q` (name fragment, e.g. "berkshire"), `cik` (SEC CIK, any width) and/or `manager_type` (from "institutional_investor_types"). Managers as {cik, name, category, current_portfolio_value}, largest first. The value is the 13F cover-page total, not AUM, and can include options. Page with `limit` and `offset`. Pass the `cik` values, never the names, to get_13f_position_changes, get_13f_cik_allocation or a custom report's `institutional_ownership` - "sectors" -> valid `sector` filter values - "institutional_investor_types" -> investor CATEGORIES (not managers) — the keys `screen_stocks(institutional_ownership=...)` filters on, and the `manager_type` values for "institutional_managers" and get_13f_ownership_by_manager_type - "countries" -> covered countries - "fiscal_quarter" -> the most recent fiscal quarter being reported - "market_cap" -> valid `market_cap` buckets (Small-cap, Medium-cap, Large-cap, Mega-cap) - "intraday_frequency" -> supported intraday chart frequencies - "technical_indicators" -> indicator names accepted by `get_technical_indicator_data` (rsi, macd, sma_50, ...); an unknown indicator there returns a 400 listing this set - "tickers" -> the full covered symbol universe as bare symbols — equities PLUS ~420 market indices as caret-prefixed symbols (^GSPC, ^VIX, ...). NOTE: thousands of names — a large payload - "tickers_with_names" -> {symbol, company_name} pairs for the company universe ONLY — market indices are NOT in this list, only in "tickers" (an even larger payload) - "data_feeds" -> the datasets `download_dataset` accepts, as {dataset, description, returns ("rows" or "download_links"), date_field, event_types (valid filter, null = unsupported)}. Call this, not download_dataset (3 calls/week), to answer "what feeds are there" Returns `options` (the values for that kind). Authoritative and never stale: it reads the backend's live config, not a hardcoded list. Public — no auth required.
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  • Preview edits to existing Meta campaigns, ad sets and ads without changing Meta: status, archive, delete, name, budget, bid (including minimum ROAS), schedule, spend limit, targeting, ad set settings and set_creative (ad text, links and call to action). At most 10 creative edits per plan. Targeting is patched; ads_search_targeting finds IDs and detail: full returns the full resulting audience. set_creative covers standard, placement-customised, multi-media and carousel creatives, with per-card headline, description and link through cards[{card, headline, description, link}]; per-card links are refused on placement-customised carousels. Flexible, catalog, partnership, language and segment customisations are refused with a reason. To change an image or video, create a new ad in the Uploader. Returns the planned changes, warnings (for example ENGAGEMENT_RESET when likes, comments and shares stay with the old post, or NO_CHANGE when a value already matches Meta), requiredConfirmations and settings: the account's edit mode and approval rule, with settings.launch as the separate permission for new-ad launches. In Apply Directly mode it returns a planId that ads_edit_apply takes for 15 minutes. In review mode it queues the changes in the Agent Inbox and returns reviewId and reviewUrl with no plan; the changes are applied when approved there, and nothingQueued: true means every change already matched. reason and runTitle label the suggestions in the Agent Inbox, and a per-operation reason overrides the top-level reason; reviewId adds them to an existing review, moving identical pending suggestions into it, and without reviewId identical pending suggestions are reused instead of queued twice. idempotencyKey makes a repeated request return the same preview (PREVIEW_IN_PROGRESS while the first is still running). Reviews stay readable for seven days after their last update; ads_list_inbox_history keeps 90 days.
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  • List ad sets in a campaign, or across the whole ad account when campaignId is omitted. To find ad sets by name without knowing their campaign, omit campaignId and pass search (name contains); each row then includes campaignId and campaignName. status defaults to 'active', so pass 'all' to include paused ad sets. Returns one bounded page; follow paging.after for more. Use fields to return only the columns you need; large pages are trimmed to fit the output budget.
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  • Projects overview of the caller's board: one entry per top-level epic with recursive descendant progress (done %, counts by status, blocked, points, sub-epics, last activity), plus a triage strip of unrouted work (backlog + unassigned root tickets). Read-only, recomputed live. Use this before planning a session to see initiative health at a glance; use ticket_list/ticket_get to drill into any id it returns. Refuses more than 10,000 source tickets, 500 emitted root/sub-epic entries or a 256 KiB UTF-8 result; never returns partial totals.
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  • Daily burndown series replayed from the append-only ticket ledger: per-day scope (existing, non-cancelled), remaining, done levels plus added/completed flows, with a summary. Optionally scope to one epic's current subtree via root_id. Backfill (legacy_snapshot) rows seed state but never count as additions. days clamps to 7-180 (default 30). Optionally pass target_date to overlay a straight-line plan: an ideal series descending from the window-start remaining to zero on that date, for reading actual against plan. Alternatively pass baseline_id for exact frozen-task planned/actual JSON with coverage; this uses the same cohort and excludes epic estimates and later additions.
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  • OPERATOR ONLY: cross-owner analytics for the META COUNCIL PLATFORM itself — every account added together, NOT the caller's workspace (use get_workspace_metrics for that). Requires both the platform:admin scope AND an ADMIN_EMAILS operator account; everyone else gets a permission error. Returns content-free aggregates only: account counts, 30-day active owners, session counts by status and token totals, ticket open/done, deal pipeline and outstanding invoice amounts separated by recorded currency without FX conversion and with missing coverage stated, overdue invoice counts, feedback backlog, per-pillar adoption, the busiest panel slugs, and a per-account activity table (email and counts). It never returns query text, answers, feedback bodies, deal or invoice detail, or any other text a user typed — the aggregate reports how much, never what about. Note that open + done need not equal the ticket total: cancelled tickets are neither.
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  • Replace an owned scenario's content, compare-and-swapping on expected_version. Revisions are append-only: validation runs already recorded keep naming the exact version and hash they were proven against, so revising never rewrites past evidence — it does mean the epic needs a fresh passing run against the new version. A stale expected_version conflicts rather than overwriting a concurrent edit.
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  • OPERATOR ONLY: visitor traffic for the META COUNCIL PLATFORM's own website — every visitor added together, NOT the caller's workspace (use get_workspace_metrics for that) and NOT the business rollup (use get_meta_council_platform_metrics for that). Requires both the platform:admin scope AND an ADMIN_EMAILS operator account; everyone else gets a permission error naming which of the two requirements failed. Covers a window of whole UTC days ending today, set by days. Returns human total views, unique visitors, the authenticated/anonymous split, the busiest public paths, the busiest in-app sections, top referrers and a per-day series, separate bot page/section view counts, plus the window it covers so the caller need not track what it asked for. Page paths and in-app sections are counted separately and are not comparable to one another. It never returns per-visitor rows, email addresses, IP hashes, user agents, session identifiers or query text — the rollup reports how many, never who. Also returns recorded account activation cohorts by creation-event source and signed-in weekly distinct account/section usage. These are prospective observations, not visitor conversion: campaign attribution, visit-to-account conversion and visit entry pages are explicitly unavailable. No named account activity is returned. Optional digest_limit includes retained weekly aggregate snapshots and delivery counts; digest_before_week pages strictly before a scheduled Monday YYYY-MM-DD. This read never captures or sends a digest.
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