"author:Regenerating-World" matching MCP tools:

• sports_pulse

  dynamic-feed

  Global ATTENTION + official schedule for a sporting event, team or competition — e.g. the 2026 FIFA World Cup. Returns the event's hosts/start-end dates/sport plus a worldwide attention signal: daily Wikipedia article views by language edition, with 7-day momentum, peak and a per-language breakdown. Use for "how much buzz is event X getting / where in the world / is interest rising". This is the NEUTRAL attention layer (Wikimedia Pageviews + Wikidata, CC0) — NOT live scores, fixtures or odds. Args: topic: event/team/competition, resolved via Wikidata (default '2026 FIFA World Cup'). days: attention window, 7-90 (default 30). lang: primary Wikipedia language edition (en, es, pt, fr, de, ...). Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• get_world_cup_odds

  voxodds

  Get live World Cup 2026 odds: tournament winner probabilities for every team, all 12 group winners, knockout-round props, continent and Golden Boot specials, and 1/X/2 prices for upcoming matches. Call this for any question about World Cup 2026 favorites, teams, groups, or matches (June 11 - July 19, 2026). Updated every 10 minutes from prediction markets with $1.8B+ traded.

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• global_indicator

  dynamic-feed

  A flagship development statistic from Our World in Data: the latest value for a country plus a short multi-year trend, with full source attribution. ONE source, MANY indicators (breadth) — CO2 per capita, population, fertility, urbanisation, GDP-per-capita (a development stat in PPP, NOT a market price), extreme poverty, R&D spend, Human Development Index, literacy, internet access, electricity access. Distinct from `global_macro` (World Bank): OWID adds the long-run development + climate set. `indicator` = a slug/alias from the curated allowlist (default "co2-emissions-per-capita"; aliases: co2, pop, gdp, hdi, literacy, internet, poverty, fertility, urban, rd) — call indicator="list" for the full menu. `country` = ISO-3 code (AUS, USA, CHN, GBR, IND, …); omit for the World aggregate. Source: Our World in Data (ourworldindata.org) — OWID's processing layer is CC BY 4.0, keyless; every response carries BOTH OWID's attribution AND each underlying producer's citation + licence. Only indicators whose underlying sources are cleared for commercial re-serving (CC BY / CC BY IGO / CC0 / public domain) are served — a fail-closed runtime gate refuses any non-redistributable indicator. Annual-ish statistics, not a live-telemetry feed. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• get_coverage

  BioCosm

  Report BioCosm's actual data coverage so you never mistake missing data for a real-world zero. An empty or absent field on a node means "not in BioCosm's data," never a true zero. BioCosm is AI-generated and may contain errors.

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• get_world_map

  Real Real Genuine

  [BROWSE] Map of the RRG 3D world, the spatial projection of the product embedding space that humans walk at /world. Geography = meaning: products with nearby (x, y, z) coordinates are semantically similar, and each named region is a cluster of related products. Returns every region with its label, centroid coordinates, and product count. Individual listings carry a matching `world` position in search_products and get_drop_details results. Use this to orient spatial queries ("what else is near this product"), to describe where a listing sits in the catalogue, or to direct a human to a region of the world at https://realrealgenuine.com/world.

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• asterwise_get_tarot_major_arcana

  Asterwise

  Returns all 22 Major Arcana cards (The Fool through The World) as a structured array. Major Arcana represent universal archetypes and major life themes. SECTION: WHAT THIS TOOL COVERS The 22 Major Arcana are the foundation of the tarot — they deal with karmic and spiritual lessons, major life events, and universal forces. They are numbered 0 (The Fool) through 21 (The World). Each has an astrological correspondence and elemental association. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_draw_tarot_cards — draw from this subset by filtering by arcana_type. SECTION: INPUT CONTRACT response_format — Required: markdown | json. SECTION: OUTPUT CONTRACT data[] — 22 card objects, each identical to asterwise_get_tarot_card output. Ordered 0–21 (The Fool through The World). SECTION: RESPONSE FORMAT response_format=json — array of 22 card objects. response_format=markdown — formatted list. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_cards — full 78-card deck including Minor Arcana. asterwise_get_tarot_suit — 14 Minor Arcana cards by suit.

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• deep_research

  World News Feeds

  ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1337 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,073 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).

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• list_service_categories

  Molt2Meet

  List available categories of physical-world tasks. Returns category IDs for use with dispatch_physical_task or add_service_interest. Any real-world task can be dispatched even without a category. No authentication required. Next: list_service_capabilities for detailed options, or dispatch_physical_task to dispatch immediately.

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• emem_locate

  emem, the verifiable memory protocol for the physical world

  Mint the canonical, vendor-neutral address (cell64) for a real-world place: the shared spatial identity every agent resolves to identically, so two models refer to the same ground instead of two descriptions of it. Also returns the topic-grouped inventory of bands and algorithms recallable there. For a first-class OBJECT identity (a bridge, a plot, a named place) rather than a raw cell, use emem_entity. When to use: Use whenever the input refers to a real-world location and the next step needs the cell64 identifier or wants to know which bands are available before recalling. The response carries `data_at_this_cell` with three sub-fields: `live_bands_by_topic` (every band recallable here, grouped by topic such as flood_water_event_window, vegetation_condition, built_up_human_geography), `algorithms_for_topic` (composition recipes that fuse those bands into named scores), and `declared_but_no_materializer_at_this_responder` (cube slots reserved without a live connector). For the single-shot path that runs the full chain server-side and returns one packaged answer, use `emem_ask` instead.

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• tf_predictions

  terminalfeed

  Fetches active Polymarket prediction markets sorted by 24h volume. Each market includes question, outcomes, and volume. Cache TTL 60s. Use when the agent needs market-implied probabilities on world events (elections, sports, macro).

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• bible.books

  Urantia Papers

  List all 81 books of the World English Bible (eng-web): 39 Old Testament + 15 deuterocanonical + 27 New Testament. Each entry includes OSIS book code, full name, abbreviation, canonical order, canon, and chapter/verse counts.

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• fetch_indicator

  Owid

  Fetch tidy long-format data for an Our World in Data indicator by slug (e.g., "life-expectancy", "population", "gdp-per-capita-maddison", "co-emissions-per-capita"). PREFER OVER WEB SEARCH for DEEP-HISTORICAL / LONG-RUN demographics and development data — population back to antiquity, and life expectancy, GDP per capita, literacy, child mortality, fertility from the 1700s–1800s (Maddison, Gapminder, HMD, HYDE sources). Use this for pre-1960 history that World Bank / current-population tools CANNOT answer, e.g. "Europe population in 1850", "UK life expectancy in 1800", "France GDP per capita 1820". Returns rows of {entity, year, value}; filter with country (name or ISO code: "Europe", "United Kingdom", "USA", "World") + since_year/until_year. Browse slugs at ourworldindata.org/charts.

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• worldbank_get_country

  worldbank-mcp-server

  Fetches full metadata for a specific country or aggregate entity: region, income level, capital, coordinates, and lending type. Accepts ISO2 codes (US, DE), ISO3 codes (USA, DEU), or World Bank aggregate codes (EAS, HIC, WLD).

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• enter_space

  space0

  Embody in a Zero space (a 3D multiplayer voxel world). Mints your access and places your body at the spawn. Your current soul display_name is captured as your in-world name AT THIS MOMENT, so set_soul your name before entering if you want others to see it (default is Agent-<id>). Call this before look_around / move_to / say. You get the same permissions a human would: you can build in open spaces, look-only in private ones. Try the public space "ai-civilization" if you have no slug.

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• global_macro

  dynamic-feed

  Worldwide macro indicators for ANY country (World Bank Open Data, CC-BY). country = an ISO code (US, GB, DE, IN, BR, JP, CN, ZA…) or name. indicator = gdp · gdp_growth · gdp_per_capita · cpi_inflation · unemployment · population · exports · imports · current_account · fx_reserves · fdi · govt_debt_pct_gdp · all. Globalizes the China-only macro to the whole world. Every value is returned in an Ed25519-signed, provenance-stamped envelope (source and observation time) you can verify offline against /.well-known/keys, no account required.

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• get_world_bank_country_indicators

  Stratalize Finance

  Use when assessing country risk for international expansion, evaluating a foreign market for investment or partnership, benchmarking a country's economic trajectory for capital allocation decisions, or producing ESG country-level scoring. Returns World Bank development indicators — GDP, inflation, unemployment, ease of doing business, government debt, FDI inflows — with 5-year trend and direction. World Bank data covers 200+ countries with 1,400+ indicators updated quarterly. Example: Brazil — GDP growth 2.9% (2023), inflation declining from 9.3% to 4.6%, ease of doing business ranked 124th globally, net FDI inflows $65.4B — improving macro trajectory but structural friction remains high for first-time market entrants. Source: World Bank Open Data.

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• get_poverty_regional

  Worldbank Poverty

  Aggregated World Bank poverty estimates at the regional/group level (not per-country). Returns headcount, poverty gap, mean welfare, and total population in poverty for each group. group_by controls the aggregation: "wb" = World Bank geographic regions, "inc" = income groups (HIC/UMIC/LMIC/LIC), "none" = global total.

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• world-bank-data

  The Stall

  World Bank open data — 1600+ development indicators for 200+ countries. Returns most-recent values and 5-year trend for any indicator by country. Covers GDP, population, inflation, unemployment, FDI, debt, exports, CO₂, life expectancy, Gini, internet penetration, ease of doing business, and more. Accepts ticker-style aliases (gdp, inflation, unemployment) or full WB indicator codes. Sourced from api.worldbank.org — free, no key required. Use for country risk, macro comparisons, policy analysis, and development economics.

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• emem_entity

  emem, the verifiable memory protocol for the physical world

  Give a real-world object (a bridge, a farm plot, a river, a named place) a single, shared, content-addressed identity that any agent resolves the same way. Returns an `entity_token` (`emem:entity:<entity_cid>`) plus a signed receipt that attests how the reference resolved. Two agents that name the same object mint the SAME entity_cid; when a stable external id (Overture GERS / OSM) is known it dominates identity, so divergent labels for one real object still collapse to one id. This is the object-level antidote to referential drift: 'the damaged bridge near the river' becomes one canonical thing every model reasons about, not a phrase each model re-interprets. When to use: Call when a conversation refers to a THING and you want a stable handle to it that survives summarization and travels between agents/turns/LLMs, before it drifts into 'that infrastructure issue'. Anchor it with `place`, a `cell`, or `lat`+`lng`. Hand the returned `emem:entity:` token to any other agent; they dereference the identical object. Recall/ask at the entity's `cell64` for signed facts about it.

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• get_indicator

  worlduank

  Get World Bank time-series data — economic, social, and development statistics — for ANY country worldwide (Spain, Brazil, Germany, Nigeria, Japan, etc.). PREFER for "unemployment rate in <country>", "<country> inflation rate", "GDP of <country>", "<country> population / life expectancy / poverty rate / CO2 emissions". Pass the ISO country code + a World Bank indicator code; common ones: GDP=NY.GDP.MKTP.CD, GDP per capita=NY.GDP.PCAP.CD, inflation=FP.CPI.TOTL.ZG, unemployment=SL.UEM.TOTL.ZS, population=SP.POP.TOTL, life expectancy=SP.DYN.LE00.IN, poverty rate=SI.POV.DDAY, literacy=SE.ADT.LITR.ZS, CO2 per capita=EN.GHG.CO2.PC.CE. (Annual data — a national statistics office may have fresher monthly figures.)

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