"Improving the Intelligence of Large Language Models" matching MCP tools:

• openmeteo_get_climate

  open-meteo-mcp-server

  Long-range climate projections from bias-corrected daily CMIP6 models, covering 1950-01-01 to 2050-12-31 at any coordinate. Answers "what will conditions look like through 2050?" — the future-projection counterpart to openmeteo_get_historical (ERA5, what happened). Daily resolution only. Available models: CMCC_CM2_VHR4, FGOALS_f3_H, HiRAM_SIT_HR, MRI_AGCM3_2_S, EC_Earth3P_HR, MPI_ESM1_2_XR, NICAM16_8S. A model name outside that list is sent upstream rather than rejected here, so a model Open-Meteo adds later still works; if upstream rejects the request, the error names the offending model on its own rather than the whole requested list. With 2+ models each variable appears once per model with the model name as suffix (e.g. temperature_2m_max_CMCC_CM2_VHR4); a single or omitted model returns plain variable names. Not all models carry all variables — missing combinations return null. Multi-decade daily pulls across several models produce thousands of records and spill to DataCanvas for SQL querying when canvas is enabled, returning a bounded preview with truncated: true when it is not.

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• get_facility_risk_delta

  DC Hub — Data Center & Power Intelligence

  Use when a user asks what has CHANGED in a facility's (or its market's) risk profile recently — "has this site gotten riskier lately?", "which way is this market moving?" — a temporal question static-trained models can't answer. Returns the REAL DCPI market-health delta (excess-power score change over the window, direction improving/worsening/flat) from DC Hub's history-preserving daily snapshots. INTEGRITY: only DCPI market-health has a short-term temporal series; the site-hazard dimensions (FEMA disaster / USGS seismic / NOAA climate / WRI water) are DECLARED static (they don't change week-to-week) with a pointer to the point-in-time tool — never a fabricated week-over-week delta; no snapshot history → coverage:unavailable. Params: facility_id (a discovered-facility id or slug) OR market (a market name/slug), since (e.g. "7d"/"30d", default 7d). Returns {facility, dcpi_market_health:{delta, now, direction, coverage}, static_dimensions{...}, summary}. For the current point-in-time risk (not the change) use get_composite_site_score / get_disaster_risk / get_climate_intel.

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• exponent_calc

  calc.engineer

  Compute the result of raising a base to an exponent (base^exponent). Handles positive and negative exponents, fractional exponents, and zero. Returns the numeric result and a scientific notation string for very large or very small results. Useful for compound interest calculations, exponential growth/decay models, physics power laws, and combinatorics. The inverse of log_calc; chain with scientific_notation for formatted display of extreme values.

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• get_facility_risk_delta

  DC Hub — Data Center Intelligence MCP Server

  Use when a user asks what has CHANGED in a facility's (or its market's) risk profile recently — "has this site gotten riskier lately?", "which way is this market moving?" — a temporal question static-trained models can't answer. Returns the REAL DCPI market-health delta (excess-power score change over the window, direction improving/worsening/flat) from DC Hub's history-preserving daily snapshots. INTEGRITY: only DCPI market-health has a short-term temporal series; the site-hazard dimensions (FEMA disaster / USGS seismic / NOAA climate / WRI water) are DECLARED static (they don't change week-to-week) with a pointer to the point-in-time tool — never a fabricated week-over-week delta; no snapshot history → coverage:unavailable. Params: facility_id (a discovered-facility id or slug) OR market (a market name/slug), since (e.g. "7d"/"30d", default 7d). Returns {facility, dcpi_market_health:{delta, now, direction, coverage}, static_dimensions{...}, summary}. For the current point-in-time risk (not the change) use get_composite_site_score / get_disaster_risk / get_climate_intel.

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• trend_analysis

  Hermes — Air Quality Intelligence

  Analyse the long-term trend in a pollutant near a location. Uses Theil-Sen slope estimation with Mann-Kendall significance testing to determine whether air quality is improving, worsening, or stable. Robust to outliers and missing data. Returns a 'summary' with plain-English trend description and statistical details. Present the summary to users first. Args: location: Postcode, place name, or "lat,lon". pollutant: Pollutant to analyse — "NO2", "PM2.5", "PM10", "O3" (default "NO2"). years: Number of years of data to analyse (default 5, range 2–5). Requests outside this range are clamped; the response includes ``metadata.years_clamped`` and a note in ``summary`` when so.

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• burnrate_optimize

  plith

  Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceeds your budget. Returns the optimized plan with substituted models, new per-step costs, total savings, and whether the target_budget is met. Optionally set target_budget to constrain the optimization. Costs 1 credit.

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• list_domains

  OpenDataModels MCP server

  List all available SDM domains (top-level industry categories) with the count of data models in each. Use this as the entry point when the user wants an overview of what sectors are covered, or before calling list_models_by_domain. No parameters required. Example: list_domains({})

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• get_region_signal

  Horizon AI Intelligence

  The current AI signal for a region (china, korea, japan, or eu) — recent, relevance-scored items on that region's models, labs, and analysis, ranked by momentum. Includes local-language press translated into English. The canonical regional tool; get_china_signal is a preset of this with region "china". Returns titles, sources, and links.

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• gdelt_get_coverage_breakdown

  gdelt-mcp-server

  Break down news coverage volume over time by source language or source country, returning a multi-series time series (one series per language or country). Shows which countries or languages drove early vs. late coverage — useful for tracing how a story propagated geographically or across language communities. Returns up to 10 series by total volume and aggregates the rest into an "Other" bucket, naming every series it folded in there under otherSeriesLabels — pass any of those labels back as the series input to get that series complete, ranked or not. Values are normalized: each point is the topic's share of media output, not an absolute article count. Small media markets with concentrated coverage therefore rank above large markets with diverse output — a high value means the topic dominated that source's coverage, not that it published the most articles. Use breakdownBy "country" with the signal-detection chain to map geographic attention, or "language" to detect non-English media surges.

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• generate_text

  Sats4AI - Bitcoin-Powered AI Tools

  Generate text using frontier AI language models. Pure per-character pricing (no minimum): Kimi K3 (best, ~10 chars/sat, 1M context, vision support, default), GPT-OSS-120B (standard, ~1000 chars/sat, 119 languages, best value). Rates are BTC-pegged and re-quoted hourly, so treat them as approximate — the 402 challenge is the authoritative price. Supports document Q&A via fileContext and vision analysis via imageBase64 (best model). Stable endpoints — models upgrade automatically. Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='generate_text' and the exact prompt.

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• get_source_accuracy

  Crank Protocol

  Historical directional hit-rate of each intelligence source (free read). Returns per-source accuracy stats for the intelligence signal sources (Coin Bureau / YouTube, RSS, Fear & Greed API, on-chain), so an agent can weight a source's calls by how often its bullish/bearish reads have played out. Each source carries a ``windows`` map over rolling 7d/30d/90d periods, each with total resolved calls, correct/incorrect counts, and accuracy_pct; sources with no resolved calls yet are omitted, and results are ranked by longest-window accuracy. windows optionally narrows the periods (subset of [7,30,90]); source_type optionally filters by source kind (youtube, rss, api, on_chain). Accuracy = the directional call vs the realised Birdeye price over the prediction window. Past accuracy is not a guarantee. Not financial advice. Workflow: INTELLIGENCE step -- pair with get_market_briefing to discount or trust a signal by its source's track record before sizing a position.

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• hsh_describe_data_need

  HSH Data-on-Demand

  FREE. Describe any data need in plain language and receive an instant firm quote from HSH Intelligence Data-on-Demand: price in USDC, scope, a frozen quote_ref, and a pay_url. Pay the quote via x402 (USDC on Base or Solana) at the pay_url to place the order; delivery in 24h. Use this BEFORE purchasing custom data.

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• compute.models

  DPX — Institutional Cross-Border Settlement

  List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.

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• search_language

  GitHits

  Use before `get_example` only when you need to force a language and are unsure of GitHits' exact language name. Finds supported language names and aliases; returns up to 5 matches. Default output is one language per line; pass `format: "json"` for the structured array.

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• intelligence.gender_risk

  DPX — Institutional Cross-Border Settlement

  Gender Risk & Opportunity Intelligence — maps the structural relationship between GBV prevalence, legal discrimination, female labour force participation, and economic outcomes across 18 countries. Returns two independent scores: gbvRiskScore (0–100 suppression risk — high GBV → female LFPR suppression → GDP drag → fiscal stress → sovereign risk premium) and opportunityScore (0–100 reform upside — improving GBV indicators, closing LFPR gender gaps, and strengthening legal rights precede FDI inflows and consumer credit expansion). Five transmission mechanisms. Live FRED economic stress feedback. AI synthesis. Data: WHO GHO, World Bank WDI, FRED. 12h cache. No input required — GET.

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• compute.models

  DPX — Institutional Cross-Border Settlement

  List all AI models available through DPX Compute. All models are free-tier (no token cost) — routed via OpenRouter. Returns model IDs, provider, capability strengths, context window, and speed tier. Use this before compute.route to understand what models are available and pick the right one for a task. Free.

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• language_lookup

  Agent Bits

  Resolve an ISO 639-1 language code such as en, fr, or ja to its English name and native name when you need language metadata from a two-letter code. Use when: - What language does ISO 639-1 code ja refer to? - Get the native name for language code fr - Resolve a two-letter language code to its English and native names Do not use when: - Translate text between languages - Detect the language of arbitrary free-form text - Look up country languages from a country code (use country_lookup)

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• language_lookup

  Agent Bits

  Resolve an ISO 639-1 language code such as en, fr, or ja to its English name and native name when you need language metadata from a two-letter code. Use when: - What language does ISO 639-1 code ja refer to? - Get the native name for language code fr - Resolve a two-letter language code to its English and native names Do not use when: - Translate text between languages - Detect the language of arbitrary free-form text - Look up country languages from a country code (use country_lookup)

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• get_ml_trends

  SnowSure — Snow & Ski

  Fetch SnowSure-unique ML/AI trend datasets from the public REST API. Use for powder-day leaders, bluebird-day leaders, bluebird predictions, improving/stable/declining score pulse, per-model accuracy weights, daily SnowSure score component history, ML extended outlook (days 8–14), global forecast trust, and powder/bluebird event logs. Start with dataset=catalog. Prefer get_insights for narrative intelligence cards; use this for raw rankings and time series.

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• transcribe

  x402-farm

  Transcribe the actual audio of a YouTube, TikTok or Instagram video with Whisper large-v3. Not caption scraping: the audio is downloaded and run through speech recognition, so it works on videos with no subtitles, in any language, and on TikTok and Instagram where no caption track exists at all. Extraction runs from a real residential IP, reaching sources that refuse datacenter ranges. Returns full text plus sentence-level timestamps. — $0.02/call, paid per request via x402 (USDC).

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