Cliometry provides AI agents with climate and environmental data for analyzing emissions, sustainability metrics, and climate-related risks. Specific data sources and supported analysis tools could not be verified from the available documentation.
ChatGPT is OpenAI's assistant. It can add custom MCP servers as plugins, which lets it call your tools from a conversation.
This guide connects Cliometry MCP to ChatGPT through MewCP, so ChatGPT can use Cliometry MCP's 9 tools, like get_realized_multiple, get_adr_premium and get_vix_band.
Cliometry MCP needs no credentials, but every call still runs through MewCP: scoped, checked and logged like any other.
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Full Google Search Console control for search analytics, sitemaps, sites, and URL inspection, directly from your AI workflows. This MCP server exposes the Google Search Console API for querying search traffic data, managing sitemaps and site properties, and inspecting the indexing status of any URL. It is built for SEO teams monitoring search performance, developers automating sitemap and property management, and analysts pulling search traffic data into reporting pipelines.
Manage courses, assignments, rosters and student activity within Google Classroom. Create learning workflows, track submissions and organize educational content across classrooms.
AI's impact on real-world sectors, as a structured record for agents: source-linked headlines placed in one of 18 sectors (healthcare, law, retail, government, defense and more), with outlet, country and time, since June 2026
MoSPI MCP Server provides AI agents access to official Indian government statistics from the Ministry of Statistics and Programme Implementation, covering 500+ indicators across employment, prices, industry, national accounts, health, education, trade, and more.
Other clients
Cliometry MCP works the same way in every client below. Pick yours for its own guide.
Free to start.
Scope access to the exact Cliometry MCP tools and accounts you allow, work and personal side by side. Anything outside that scope is refused at call time, even when the model asks for it.
Each call is logged: what ran, on which account, and whether it worked. Failures come back structured, so the agent knows whether to retry instead of guessing.
Every app sits behind one secure MCP URL, and there is no server for you to deploy or keep running. Add an integration in the dashboard and it is live on the next call, with no config to change and no new credentials to wire up. Tools load only when a task needs them, so your agent stays fast and focused however many apps you connect.
In ChatGPT, open Plugins and choose Add custom MCP.
Name it "mewcp" and paste the server URL.
Click Connect. A sign-in window opens; sign in to MewCP and approve access. No API key needed.
Start a new chat and ask: "List the tools you can use from MewCP." You should see your connected apps.
Use this when someone asks why a leveraged or inverse ETF did not deliver its stated multiple - "why is my 2x ETF down more than 2x?", "how much did leverage decay cost me?", "is a 3x ETF really 3x over a month?". The realized value as of the latest trading day is not in training data; this returns it, updated daily. Measures how many times a leveraged or inverse ETF actually moved relative to its stated daily target multiple, over 20/60/252 trading-day windows. Returns the benchmark and product cumulative returns, the design-implied expectation, the gap in pp, realized annualized volatility of the benchmark, and an approximate volatility-drag term. Covers KODEX Leverage and KODEX 200 Futures Inverse 2X (KRX-listed) plus SOXL and TQQQ (US-listed). This is a mechanical decomposition of past realized returns; it does not predict future multiples and is not a judgment on which product is better. The ratio is withheld when the benchmark return is near zero, because it diverges there.
Use this when someone asks whether SK Hynix is priced differently in the US and Korea - "is the ADR at a premium right now?", "why is SKHY more expensive than 000660?", "how wide is the gap today?". Today's reading is not in training data; this returns the latest measured value. Reports how many percent the US ADR of SK Hynix (SKHY), converted to KRW, sits above or below the Seoul-listed ordinary share (KRX:000660). Pass a date for that trading day, or omit it for the most recent reading. Daily series accumulated by Cliometry; delayed reference data.
Use this when someone asks about the current volatility regime - "what is VIX today?", "is the market calm or fearful right now?". The latest close is not in training data. Returns the CBOE Volatility Index (VIX) close and a fixed-threshold regime label (calm / watch / fear). The label states where the index currently sits; it does not indicate what markets will do next and is not a trading signal.
Use this when someone asks how the US-Korea gap for SK Hynix has moved over time - "has the ADR premium been widening?", "what was it last month?", "is today unusual versus the past 60 days?". Answering needs the actual daily series, which is not in training data. Returns the daily SK Hynix ADR premium series (US ADR SKHY converted to KRW versus the Seoul-listed ordinary share, KRX:000660) as an array, plus a summary. Use this instead of calling the single-value tool repeatedly: one call covers the whole span. Defaults to the most recent 60 sessions; pass from/to for an explicit range or window for a different count. The array is returned as structured data, not as text, so it does not consume the conversation context.
Use this when someone asks whether leverage decay is getting worse or better - "has the realized multiple been drifting?", "was decay worse in 2022 than now?", "show the history, not just today". This returns the measured daily series back to 2016. Returns the rolling 60-trading-day realized multiple for a leveraged or inverse ETF as a daily array, back to 2016, plus a summary. One call replaces many single-window calls when the question is about how the multiple has moved over time. Only the 60-trading-day window has history; 20 and 252 exist as latest-snapshot values only. Points where the source could not compute the ratio are returned as null with a stated reason rather than being dropped. The array is returned as structured data, not as text, so it does not consume the conversation context.
Use this when someone asks what a leveraged ETF looked like at a specific past date - "what would the multiple have been if I bought in March?", "compare last June to now". It replays published measurements at the chosen date; it does not simulate hypothetical scenarios or accept amounts. Replays already-published measurements for a leveraged or inverse ETF at a trading day the user picks, and optionally against a second day for comparison. It replays recorded history only: it does not generate scenarios, does not extend beyond the published record, and takes no amount of money. Intended for the interactive panel, where the person moves the date; the assumptions returned are the ones that person chose.
Use this when someone asks about measured relationships in Korean memory semiconductors - "does Micron lead SK Hynix?", "how correlated are they?", "what is the actual number rather than a guess?". These are constants measured from our own published series. Returns the measurement constants Cliometry has already published in its guides — the Micron/SK Hynix same-day return correlation, the observed next-day frequency after a large Micron move, and the VIX regime band thresholds — each with the guide URL it was published in. These are recorded measurements of past data with their sample sizes stated, not rules and not indicators. Correlation is not causation and an observed frequency is not a guarantee.
Use this when someone asks where Korean semiconductor sectors stand relative to the market right now - "which sector is leading or lagging?", "where do semis sit in the rotation today?". The current snapshot is not in training data. Returns the latest Relative Rotation Graph snapshot for Korean semiconductor-related sectors — per-sector relative-strength ratio, relative momentum, and quadrant. Describes where each sector currently sits against the benchmark; it does not forecast direction.
Use this when someone asks what a move in the SK Hynix ADR premium was made of - "the premium jumped, was that the ADR or the won?", "how much of the widening is FX?", "split the change into its parts". The premium is defined multiplicatively, so a change in it decomposes exactly into three additive terms: the US ADR (SKHY), the USD/KRW rate, and the Seoul-listed ordinary share (KRX:000660). Returns the published premium change in pp for a range, and the three terms in pp that sum exactly to it. Omit from/to for the most recent completed session against the one before it. This is an arithmetic identity, not a causal attribution: it states how the published change splits, not why any of the three moved, and it is not a forecast. Ranges whose endpoints carry different ADR ratios, and ranges whose premium change is below rounding, are returned undecomposed with a stated reason rather than estimated.
