"Flood" matching MCP tools:

• flood

  Open Meteo

  "Flood risk for [river]" / "river discharge forecast" / "will [river] flood" / "water levels at [location]" — daily river discharge forecast from the GloFAS global flood model. Returns predicted m³/s discharge up to 30 days ahead for any river-bearing lat/lng worldwide. Use for flood risk assessment, agriculture planning, hydrology research.

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• polymarket_fill_risk

  flood

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

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• station_search

  Japan Toilet & Accessibility

  Discover Japanese train stations by describing what you want around them, in English or Japanese — "朝ラーメンが食べられて車椅子トイレがある駅", "terminal station with late-night ramen", "水害リスクが低くてラーメンが多い駅". Semantic search over 9,035 station profiles (lines/terminal size, ramen density & styles, in-station accessible-toilet equipment, official hazard categories, ridership) with hybrid metadata filters — the filters guarantee the constraint, the embedding ranks by fit. Filter intent in the query text (朝ラー/深夜/おむつ/車椅子/水害リスク低…) is auto-applied (filter_source: inferred); explicit params win. Water-hazard intent (水害/洪水/浸水/高潮…リスク低) expands to flood rank AND storm-surge zone; 液状化/地盤 intent filters on the official liquefaction-tendency category; results carry risk_notes when other official hazard categories are high. Inferred facility filters with partial data coverage (おむつ/車椅子 — Tokyo-only data) BOOST confirmed stations instead of excluding unknowns (see soft_filters); explicit params remain strict. Taste/quality words (うまい, "good food", delicious…) are not evaluated (no review data); ramen ranking reflects shop density and style variety only. name_contains gives exact substring matching on station names (日本語/romaji) when the name itself is the requirement. Coverage notes: toilet stats = Tokyo stations only; ridership = Greater Tokyo operators only; hazard = official MLIT categories relayed as-is, NOT a safety judgment. Role split: station_search finds candidate stations — then get_toilet_by_station / search_ramen / get_station_hazard / get_station_context for detail on one station.

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• emem_ask

  emem, the verifiable memory protocol for the physical world

  Single-shot free-text answer about a real-world location, backed by signed satellite/elevation/water/built-up receipts. Forwards a place mention plus a question; runs the locate → recall → algorithm chain server-side; returns one packaged envelope. When to use: Use when the question concerns a specific real-world place and a packaged, citation-bearing answer is preferable to manual primitive composition. Forward the user's question verbatim as `q` plus the location as `place` (free text), `cell` (cell64), or `lat`+`lng`. The server resolves the location, classifies the question to a topic, recalls every relevant band (auto-materializing Sentinel-2 / Sentinel-1 / Cop-DEM / JRC GSW / Overture / weather on miss), surfaces the algorithm recipes that compose those bands into named scores, and returns a single envelope with `topic_routing`, `facts`, `algorithms_for_question`, an optional Sentinel-2 RGB scene URL, and a `caveats` block (grid resolution, revisit cadence). All facts are signed by the responder; the signed `receipt` (and its content-addressed `fact_cids`) is surfaced at the envelope ROOT, `response.receipt` / `response.fact_cids`, exactly like every other primitive, and is also mirrored under `facts_summary.receipt` for back-compat. Set `include_image: true` to bundle the latest cloud-free Sentinel-2 thumbnail. Out-of-scope questions return `topic_routing.matched_topic: null` plus the full inventory so the caller can route elsewhere. Example arguments: {"q":"is this neighbourhood flood-prone for a flat purchase","place":"Ashok Nagar, Ranchi"}

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• deep_research

  Food 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 1494 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 5,708 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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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• search_within

  flood

  Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).

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• entity_profile

  flood

  "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).

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• polymarket_arbitrage

  flood

  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}. 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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• pipeworx_feedback

  flood

  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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• remember

  flood

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

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• disaster_recovery_profile

  LiveDataLink

  One-call disaster-history and recovery read for a US area (county or place), keyed by NAME + state - distinct from location_risk_report, which scores a single site by address/lat-lon. Joins FEMA's OpenFEMA disaster declarations (the area's federally-declared disaster history: incident types, frequency, most-recent event, and the federal-assistance signal - which programs, Individual Assistance / Individuals & Households / Public Assistance / Hazard Mitigation, were authorized) with optional US Census ACS county population for exposure context (keyed off the FIPS codes the FEMA records carry; needs a Census key and degrades gracefully) and an optional best-effort parcel record for property context when an address is given (Maryland statewide / Texas-Harris County only). Returns a readable profile with a headline banding the area's disaster exposure LOW / MODERATE / HIGH from the declaration record. The FEMA leg is keyless and is the core signal; a source that fails is noted, not fatal. INFORMATIONAL public-record synthesis, NOT an insurance rating, a property flood-risk score, or a professional risk assessment.

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• noaa_marine_get_current_profile

  noaa-marine-mcp-server

  Observed ocean-current depth profile from an NDBC ADCP buoy: the most recent measurement of current speed and direction at each depth bin. Returns depth in meters, direction in degrees true (the direction the current flows toward), and speed in cm/s. Distinct from noaa_marine_get_currents, which returns CO-OPS tidal-current predictions (forecast max flood/ebb/slack) rather than these observed acoustic-Doppler measurements. A depth bin is reported whenever NDBC gives it a depth; its direction or speed is null when the sensor did not report that component. Use noaa_marine_find_stations with source="ndbc" and types=["current_profile"] to find station IDs — most NDBC stations serve no ADCP profile, so an unfiltered search returns IDs this tool cannot read.

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• noaa_get_station_info

  NOAA Tides and Currents

  Get a NOAA station's full metadata record: location, state, time zone, tide type, Great Lakes flag, capability flags, and links to available sub-resources. Optionally expand sub-resources inline via the "expand" list: - details (established/removed dates), sensors (installed instruments + elevations), floodlevels (NOS/NWS minor/moderate/major flood thresholds), benchmarks, products (available data page links), notices, disclaimers — for water-level stations - bins (ADCP depth bins), deployments — for current stations (alphanumeric IDs) Use this before requesting data to confirm what the station actually collects. For datum values use noaa_get_station_datums; for harmonic constituents use noaa_get_harmonic_constituents.

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• station_search

  Gachi Data API — Japan Station & Accessibility Data

  Discover Japanese train stations by describing what you want around them, in English or Japanese — "朝ラーメンが食べられて車椅子トイレがある駅", "terminal station with late-night ramen", "水害リスクが低くてラーメンが多い駅". Semantic search over 9,035 station profiles (lines/terminal size, ramen density & styles, in-station accessible-toilet equipment, official hazard categories, ridership) with hybrid metadata filters — the filters guarantee the constraint, the embedding ranks by fit. Filter intent in the query text (朝ラー/深夜/おむつ/車椅子/水害リスク低…) is auto-applied (filter_source: inferred); explicit params win. Water-hazard intent (水害/洪水/浸水/高潮…リスク低) expands to flood rank AND storm-surge zone; 液状化/地盤 intent filters on the official liquefaction-tendency category; results carry risk_notes when other official hazard categories are high. Inferred facility filters with partial data coverage (おむつ/車椅子 — Tokyo-only data) BOOST confirmed stations instead of excluding unknowns (see soft_filters); explicit params remain strict. Taste/quality words (うまい, "good food", delicious…) are not evaluated (no review data); ramen ranking reflects shop density and style variety only. name_contains gives exact substring matching on station names (日本語/romaji) when the name itself is the requirement. Coverage notes: toilet stats = Tokyo stations only; ridership = Greater Tokyo operators only; hazard = official MLIT categories relayed as-is, NOT a safety judgment. Role split: station_search finds candidate stations — then get_toilet_by_station / search_ramen / get_station_hazard / get_station_context for detail on one station.

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• emem_tools

  emem, the verifiable memory protocol for the physical world

  The map of emem's tool surface, and the only tool you need to find the rest. Returns the working loop in the order you walk it (name a thing, ground it, cite it, resolve it, verify it, check for drift), then every other tool grouped by the question it answers, each with its one-line trigger. Pass `name` to get one tool's full input schema and a runnable example, so you can use a tool without loading all of the descriptors into context. IF YOU ARE READING A LIST OF 16 TOOLS, YOU ARE SEEING A CURATED SUBSET OF 108, NOT THE WHOLE SURFACE. The count is served in tools/list `_meta` and `_discovery`, and most MCP hosts strip non-standard top-level fields before a model sees them, so it is repeated HERE — a description is the one field every host passes through. The Earth-observation, search, embedding and transparency-log tools are catalogued by this tool and every one of them stays callable by name through tools/call at either endpoint. When to use: Call this FIRST when you do not know which emem tool answers the question, or when you need a capability you cannot see in your tool list. This responder advertises a small core loop by default rather than its full catalog, so a tool being absent from your list does not mean it is absent from the server. Pass `q` to search by topic (`ndvi`, `cloud`, `flood`, `verify`), `name` for one tool's exact schema, or no arguments for the whole map. If you want the full catalog registered as callable tools instead, reconnect to the /mcp/full endpoint; for a one-shot answer without picking a primitive at all, use emem_ask. Example arguments: {"q":"ndvi"}

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• noaa_climate_search_storm_events

  noaa-climate-mcp-server

  Search the NCEI Storm Events Database for one calendar year — tornadoes, hail, floods, hurricanes, winter storms, heat, and every other NWS Storm Data event type, with magnitude, direct and indirect deaths and injuries, property and crop damage, and the episode and event narratives. This is a different NOAA corpus from the CDO tools on this server: it carries discrete severe-weather events rather than station observations, needs no token, and is published as one bulk file per year, so year is required. Filter with state (the full upper-case name NCEI writes, e.g. "FLORIDA" — not the postal code "FL"), eventType (the exact NWS label, e.g. "Tornado", "Hail", "Flash Flood", "Hurricane (Typhoon)", matched case-insensitively), month, and minDamageInUsd. Damage arrives from NCEI as a magnitude-suffixed string ("75.00K", "1.20M", "1.00B") and is returned as both the raw cell and a parsed dollar amount; an unreported figure is omitted entirely rather than reported as zero, and minDamageInUsd therefore excludes those rows and says how many it dropped. Results come back in the source file's own row order, paged with limit and offset, and totalCount is the true match count for the whole year.

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• deep_research

  flood

  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 1494 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 5,708 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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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• water_get_conditions

  usgs-water-mcp-server

  Get a USGS site's current reading ranked against its full period-of-record daily-mean percentiles for the same calendar day — a "how unusual is this" percentileClass (record-high to record-low), not a flood-stage or drought determination (this tool fetches no authoritative thresholds). The reading is instantaneous but the percentiles are daily-mean, so the ranking is approximate (see historicalContext.comparisonBasis). When the record is too short to rank, returns the reading with historicalContext=null instead of an error. Use water_find_sites and water_list_parameters to resolve inputs.

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• deep_research

  Uk Food Hygiene

  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 1494 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 5,708 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 — a resolvable pipeworx:// record URI, present only when the source emits one that resources/read can actually serve, so a citation you get back is always fetchable. "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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• search_signals

  Pronto

  Find signals by the words in them. Use this when you are looking for a subject — a storm name, a company, a phrase a report would print — and cannot name it as a category. When you can name it as a category instead (a country, a domain, a severity), scope_signals selects that slice exactly and does not depend on any particular word appearing; this tool ranks by word overlap and will miss a matching signal that phrased it differently. The search covers places, observations, summaries, identifiers, and the country and topic facets of each signal, so "Japan" reaches a Japanese-language article that never writes the word. A signal is returned when it contains the words you asked for. Inflections count: "flood" reaches "flooding" and "quake" reaches "quakes". Synonyms do not: the match is lexical, not semantic, so "car" does not reach "automobile" and "downturn" does not reach "recession". Use the words the source would have used. Results are ranked by how much of your query each signal contains, exact phrase matches first. When nothing contains your terms the tool returns an error rather than the closest available rows; an empty result means the wire does not carry it, not that the search gave up. Returns CWF lines.

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