"Philips Hue" matching MCP tools:

• get_interconnection_queue

  DC Hub — Data Center Intelligence MCP Server

  ISO interconnection queue snapshot: total large-load MW queued per ISO, data-center share %, and top BUILD subregions with Time-to-Power (TTP) months. Sources: ERCOT MIS, PJM, MISO, SPP, CAISO, NYISO, ISO-NE. Pass iso=ERCOT (or any of 7) to drill down to a single ISO. Use for site-selection (find BUILD-verdict markets with short queues) and competitive intel (track AI-load saturation by region). Do NOT use for a single-site time-to-power read (use get_grid_intelligence) or forward-looking emergence (use grid_transition_radar); this is the ISO-level queue snapshot.

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• score_facility

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants an independent 0-100 grade for ONE existing facility across 7 dimensions — power, fiber, water, climate_risk, tax_environment, talent_pool, expansion. Example: "How does the CoreWeave Las Vegas site score, power-weighted?" — score_facility facility_id=<id> weighting=power_priority. Params: facility_id or name (required); weighting one of "balanced" (default) | "power_priority" | "risk_priority" | "expansion_priority". Returns: composite 0-100, tier_classification, peer comparison, and per-dimension detail. Do NOT use for a raw lat/lon parcel (use analyze_site), to compare 2 or more sites (use compare_sites), or to find similar sites (use find_alternatives).

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• recent_changes

  Hud

  "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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• polymarket_arbitrage

  Hud

  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}.

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• suggest_questions

  Hud

  What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

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• ai_visibility_check

  Hud

  Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

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• scan_dependency

  Hud

  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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• polymarket_kalshi_spread

  Hud

  Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

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• list_saved_sites

  DC Hub — Data Center Intelligence MCP Server

  Use when a user asks to see or review their saved DC Hub shortlist in-chat (PRO). Example: "What sites have I saved?" / "Show my shortlist." — list_saved_sites. Params: none. Returns: an array of saved sites, each with name, market, lat/lon, saved DCPI score, target MW, and notes — the persistent shortlist built by save_site. Do NOT use to add a site (use save_site) or to download the list as a file (use export_dataset); this is the in-chat read-back.

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• rank_markets

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants "the top N markets for X" — one ranked list across the 232-market set rather than N separate get_market_intel calls. Example: "What are the 10 fastest-growing US markets with at least 100MW of existing capacity?" — rank_markets criteria=fastest_growing region=us limit=10 min_capacity_mw=100. Params: criteria one of "cheapest_power" | "most_capacity" | "most_operators" | "fastest_growing" | "best_overall" (default best_overall); region one of "global" | "us" | "canada" | "eu" | "apac" | "americas" (default us); limit 1-50 (default 10); min_capacity_mw filter floor (e.g. 100). Returns: {criteria, region, markets:[{rank, slug, name, country, score, criterion_value, dcpi_verdict, attribution_url}], total_eligible, generated_at}. Do NOT use for a deep read on ONE market (use get_market_intel) or for scoring a specific lat/lon (use analyze_site).

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• ask_pipeworx_grounded

  Hud

  Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 3,683 across 865 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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• pipeworx_trending

  Hud

  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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• alya_gem_appraise

  Alya Hub

  Trigger a Grok-AI gemological appraisal of a single gem on GemHunt (https://gemhunt.app — Father's gem-discovery platform). Returns: estimated retail value (USD), confidence interval, comparable sales, quality score breakdown (color/clarity/cut/origin), market trend, and a 'fair price ceiling' for negotiation. Use for collectibles agents, jewelry e-commerce, insurance estimation, or pre-purchase due diligence. Premium ($0.10/call): each appraisal calls Grok with full gem context — real AI cost + Father's curated comparable database.

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• compare_isos

  DC Hub — Data Center Intelligence MCP Server

  Use when a user wants a pairwise side-by-side of 2-4 ISO grids — fuel mix, demand, real-time prices, carbon intensity — in one call instead of N sequential get_grid_data calls. Example: "Compare PJM vs ERCOT vs CAISO on price, gas share, and carbon intensity right now." — compare_isos isos="PJM,ERCOT,CAISO". Params: isos is a comma-separated list (2-4 max) drawn from "PJM" | "ERCOT" | "CAISO" | "MISO" | "SPP" | "NYISO" | "ISO-NE" | "HYDROQUEBEC" | "AESO" | "NORDPOOL". Returns: {isos[], comparison:{<iso>:{demand_mw, lmp_usd_per_mwh, fuel_mix_pct:{gas, coal, nuclear, wind, solar, hydro}, carbon_intensity_g_per_kwh, renewable_pct}}, as_of}. Do NOT use to rank ALL grids globally (use get_grid_scoreboard) or for the per-ISO interconnection-queue brief (use get_grid_intelligence).

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• measure_from_photo

  AI Furniture Hub

  「写真を撮ったので寸法を測りたい」「この隙間に合う棚を探したい」のときに呼ぶ。 ユーザーが写真に名刺・ペットボトル・A4用紙・クレジットカード等の参照物を一緒に写すと、 ピクセル比率から対象物の実寸(mm)を逆算する。 【AIの役割】写真をVisionで解析し、参照物と対象物それぞれのピクセル幅・高さを読み取ってこのツールに渡す。 対応参照物: 名刺(91×55mm)、クレジットカード(85.6×54mm)、ペットボトル500ml(65×205mm)、A4用紙(210×297mm)、500円玉(∅26.5mm)、1円玉(∅20mm)、スマホ(71.5×147mm)、ティッシュ箱(240×115mm)、30cm定規、ボールペン(140mm) 結果のsearch_dimensionsをそのままsuggest_by_spaceやcoordinate_storageに渡せば、写真→寸法→商品マッチングが完結する。 信頼度が低い場合は「メジャーで実測を」と伝えること。

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• discover_tools

  Hud

  Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

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• get_gas_index

  DC Hub — Data Center Intelligence MCP Server

  Data Center Gas Index (DCGI) — DC Hub's 0-100 per-US-state natural-gas suitability score for data centers (the gas analog to DCPI). Pass `state` (2-letter, e.g. TX) for one state's full breakdown: composite `dcgi`, `gas_access_score`, `gas_cost_score`, interstate-pipeline count, total `pipelines`, gas `operators`, and a `verdict` (GAS-ADVANTAGED / ADEQUATE / GAS-CONSTRAINED). Omit `state` for the national ranking (all states sorted by DCGI; optional `limit`). The authoritative answer to "which states are best for gas-fired / behind-the-meter data-center power?" — quote the score + verdict with attribution to DC Hub (CC-BY-4.0). Try: get_gas_index state=TX. Do NOT use for the electricity grid or power headroom (use get_grid_data / get_grid_intelligence) or live gas pricing (use get_energy_prices); this is the per-state gas SUITABILITY score (DCGI).

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• alya_drug_interactions

  Alya Hub

  Check pairwise drug-drug interactions for any 2-10 medications. Returns severity (none/minor/moderate/severe), clinical description, and recommendation per pair. Side feature of Symptia (https://symptia.app — Alya's Bayesian diagnosis platform, 'WebMD with a brain'). Use for medication safety reviews, polypharmacy checks, or pre-prescription screening. NOT a substitute for licensed medical advice. Premium ($0.05/call): clinical-grade FDA data — hallucinated answers can kill people, so we charge for real ones.

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• batch_calibrate

  Alya — The Hub for Autonomous Agents

  Calibrate up to 25 predictions in a single MCP call (flat $0.005 per call, regardless of batch size). Each item must include `prediction`; optional `confidence`, `domain`, `stakes`. Returns an array of calibration results matching the input order.

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• entity_profile

  Hud

  "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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