"namespace:io.github.uxjulie-climate" matching MCP tools:

• ask_pipeworx_grounded

  climate

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 3,745 across 884 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

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• noaa_fetch_data

  noaa-cdo-mcp-server

  Fetch historical observation records from a NOAA CDO dataset for a given date range. Requires datasetId (e.g., GHCND for daily, GSOM for monthly), startDate, and endDate. Optionally scope to specific stations, locations, and data types. Date range limits per request: sub-daily and daily datasets (GHCND, PRECIP_15, PRECIP_HLY, NORMAL_DLY, NORMAL_HLY) are limited to 1 year; monthly and annual datasets (GSOM, GSOY, NORMAL_MLY, NORMAL_ANN) are limited to 10 years. For climate normals (NORMAL_*), use startDate=2010-01-01 and endDate=2010-12-31 — that is the API proxy year regardless of which 30-year period is being described. Returns flat tuples of { date, datatype, station, value, attributes }. Strongly recommended: pass units=metric or units=standard — without it, GHCND values are raw tenths-of-unit integers (TMAX=256 = 25.6°C, PRCP=12 = 1.2mm). GSOM/GSOY are already scaled.

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• remember

  Worldbank Climate

  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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• polymarket_fill_risk

  Worldbank Climate

  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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• pipeworx_trending

  climate

  What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

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• recent_changes

  Worldbank Climate

  "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

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• scan_dependency

  climate

  Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

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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.

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• deep_research

  climate

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,745 tools across 884 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• analyze_site

  DC Hub — Data Center Intelligence MCP Server

  Use when a user has ONE specific lat/lon (a parcel, a candidate site) and wants the full multi-factor data-center suitability read in one call. Example: "Score this Phoenix parcel for a 100MW build — grid, fiber, water, tax, climate." — analyze_site lat=33.45 lon=-112.07 capacity_mw=100. Params: lat (-90 to 90, required), lon (-180 to 180, required), capacity_mw (target load in MW, e.g. 50-500), state (2-letter US, optional — improves tax-incentive lookup), include_grid/include_risk/include_fiber (booleans, default true). Returns: {composite_score (0-100), verdict (BUILD/CAUTION/AVOID), grid_headroom_mw, nearest_substation_km, max_voltage_kv, fiber_carrier_count, nearest_ix_km, water_stress_score, drought_category, climate_risk_score, tax_incentive_value_usd, biggest_risk_factor, recommended_action}. Do NOT use to compare 2+ sites (use compare_sites) or to find sites that match a target (use find_alternatives).

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• search_within

  climate

  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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• analyze_site

  DC Hub — Data Center & Energy Intelligence

  Use when a user has ONE specific lat/lon (a parcel, a candidate site) and wants the full multi-factor data-center suitability read in one call. Example: "Score this Phoenix parcel for a 100MW build — grid, fiber, water, tax, climate." — analyze_site lat=33.45 lon=-112.07 capacity_mw=100. Params: lat (-90 to 90, required), lon (-180 to 180, required), capacity_mw (target load in MW, e.g. 50-500), state (2-letter US, optional — improves tax-incentive lookup), include_grid/include_risk/include_fiber (booleans, default true). Returns: {composite_score (0-100), verdict (BUILD/CAUTION/AVOID), grid_headroom_mw, nearest_substation_km, max_voltage_kv, fiber_carrier_count, nearest_ix_km, water_stress_score, drought_category, climate_risk_score, tax_incentive_value_usd, biggest_risk_factor, recommended_action}. Do NOT use to compare 2+ sites (use compare_sites) or to find sites that match a target (use find_alternatives).

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• pipeworx_feedback

  Worldbank Climate

  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). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. 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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• deep_research

  Worldbank Climate

  Grounded multi-source research in ONE call. Decomposes your question into focused sub-questions, routes each to the right one of 3,745 tools across 884 authoritative sources IN PARALLEL, and extracts a grounded answer per facet — verbatim evidence, confidence, source, fetched_at, and a stable pipeworx:// citation on every finding, with explicit gaps[] for facets the data couldn't answer (never invented). Returns a structured findings packet you can synthesize for your user; the facts arrive pre-verified. Use for broad or multi-part questions ("compare X and Y's exposure to Z", "research the regulatory + financial + market picture for ACME"); use ask_pipeworx for single lookups — it's one LLM call instead of many. Requires a Pipeworx account (sign in via GitHub at https://pipeworx.io/signup); depth:"thorough" requires a paid plan. Expect 15-60s.

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• remember

  climate

  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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• polymarket_arbitrage

  climate

  REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails. Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. `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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• recall

  climate

  Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

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• subscribe

  Worldbank Climate

  Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

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• polymarket_edges

  Worldbank Climate

  Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price. Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets. FIVE MODEL FAMILIES grouped into three response segments under by_segment: (1) MODEL_DRIVEN — crypto_price (lognormal barrier from 90d FRED log-returns) and news_momentum (GDELT 7d/21d article-volume ratio, soft signal w/ halved Kelly). (2) STRUCTURAL_ARBITRAGE — partition_overround on mutually-exclusive events; per-leg favorite-longshot bias correction with per-sport α (tennis 1.02, soccer 1.10, MMA 1.15, default 1.0); placeholder-slug filter drops will-person-X / will-team-Y / will-manager-Z / will-someone-else- backstops; partitions with >20% placeholder fraction skipped entirely. (3) CONCENTRATED_LONGSHOT — basket trade when one leg ≥75% AND ≥2 longshots ≤8% AND portfolio return ≥25:1; rare-by-design (gates relaxed Run 8 from prior 85%/5%/50:1). EVERY OPPORTUNITY carries edge_pp_net (after slippage), kelly_fraction + kelly_fraction_half (capped at 0.25), market.liquidity, market.spread_pp, market.volume, plus a 24h-move warning ("Market moved X.Xpp in 24h") when the recent move alone exceeds the edge — your edge may already be in the price. TRADEABLE-EDGE KNOBS: min_liquidity / max_spread_pp drop opportunities where edge isn't realizable; min_partition_leg_kelly filters partitions by best per-leg Kelly. RESPONSE TOP-LEVEL: by_segment{model_driven,structural_arbitrage,concentrated_longshot}, fed_candidates/fed_note (Fed bets surface here, excluded from ranking — 1m-T vs EFFR signal is unreliable at meeting-month horizons without paid OIS/SOFR-futures data), and _diagnostics{concentrated_longshot:{...funnel counters},category_counts,filter_skips} so callers can see WHY a segment is empty (top-N stale, all candidates failed gates, knob dropped them). Cached 1h at the KV level keyed on all knobs.

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• get_tax_incentives

  DC Hub — Data Center & Energy Intelligence

  Data center tax incentive packages by US state — sales-tax exemptions, property-tax abatements, income-tax credits, electricity-tax discounts, and minimum-investment thresholds. Returns program name, value (% or $), eligibility (MW/jobs), expiration date, and source statute. Try: get_tax_incentives state=VA. Covers ONE factor (tax) by US state; for a combined multi-factor site read (grid + fiber + water + tax + climate) use analyze_site.

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