# Starwell: World Data & Statistics Muse connector

From musedirectory.ai, the independent directory of Meta Muse connectors. Not affiliated with Meta.

## Starwell: World Data & Statistics

Access official economic and statistical data from 28 agencies including FRED, World Bank, and OECD.

- Record: https://musedirectory.ai/connector/starwell-world-data-statistics
- Category: Research & Data
- Developer: Adarsh4052 (https://starwell.dev)
- Muse status: Extra setup. Not in Muse's Connectors list yet. Muse can still use it: its page gives you a request to paste into Muse.
- Health: Working, 1607ms, checked 2026-09-28T08:15:59Z
- Endpoint: https://starwell.dev/api/starwell/mcp
- Auth: No account needed; Pricing: unknown
- Screening: Screened, no issues found (2026-09-24T02:25:46Z)
- Source: Found in the official MCP Registry (dev.starwell/world-data-statistics) https://registry.modelcontextprotocol.io/v0/servers?search=dev.starwell%2Fworld-data-statistics

Connects to 28 official statistical agencies (FRED, Eurostat, ECB, World Bank, OECD, Statistics Canada, ONS and others). Muse can search for economic indicators, retrieve verified data with citations, compute statistics, and answer questions that combine data across multiple sources.

Example request: "What is the current unemployment rate in Canada and how does it compare to the US over the last year?"

How to connect: Not in Muse's Connectors list yet, but Muse can still use it. Paste this into Muse: "Use Starwell: World Data & Statistics to help me. It is a free service with an MCP server at https://starwell.dev/api/starwell/mcp. It does not need an API key. Ask me before you share anything with it." Muse asks before it shares anything with the app's site. Meta does not review apps used this way, so only use ones you trust. We tested this in the Muse app on September 24, 2026: Muse used an app's link directly this way and returned a live answer.

Tools:
- list_sources: List the official statistical sources served by this data layer (e.g. Statistics Canada, FRED), with dataset/series counts, cadence notes, and terms links. Start here to see what exists.
- search_catalog: Find series across ALL sources in one call: case-insensitive search over series ids, indicator names, geographies, and dataset titles (e.g. "unemployment canada", "10-year treasury", "CPI"). Returns candidate series with units, coverage, verification status, and license, plus dat
- list_datasets: List the datasets (official releases/tables) available in one source, with coverage dates and links to the official table pages.
- get_series: Full metadata for one series: indicator, unit, frequency, geography, coverage, its VERIFICATION STATUS (passing/stale/failing/unverified from golden-value + freshness checks against the live source), recent check records, and the citation to the official table.
- get_observations: Observation values for one series. Every value carries provenance (the exact source URL it came from, retrieval time, connector version) and the envelope carries the series verification status + citation. Defaults to the latest 60 points; use start/end (YYYY-MM-DD) or latest to c
- get_series_stats: Latest value, previous, all-time min/max, mean, and change vs the previous period and vs a year ago — computed over the verified store, with the citation attached. The cheap way to answer "what is it now and how has it moved" without a full analysis run.
- create_monitor: Create a monitor: when the store's refresh lands a new period or a revised value for the series, a series.updated webhook fires to your URL with the new value, verification status, and citation. Requires an API key (free with an account at /account); webhook must be https. Manage
- list_monitors: Monitors on your API key, with delivery health (last fired, failures, active).
- delete_monitor: 
- answer: The flagship: ask a natural-language question about the served official statistics. Returns a COMPUTED answer (real Python runs in a sandbox over the verified store, nothing is estimated by a model), the Plotly chart, the Python code, citations to the official tables, and a verif
- deep_analysis: The moat: a PLANNED multi-section report on a question. The engine designs 3-4 orthogonal analyses (trend, statistics, outliers, cross-series relationships), runs real Python for each in the sandbox over the verified store, and synthesizes one decision-ready report. Every number 

Screening checks:
- MCP handshake: pass (Answered in 464ms)
- Domain against threat feeds (Cloudflare security DNS): pass (starwell.dev not flagged)
- Published packages against the OSV malicious-package database: pass (No malicious-package advisories)
- Hidden instructions or invisible characters in tool text: pass (11 tools read, nothing found)
- Inputs asking for passwords, card numbers or seed phrases: pass (None found)
- Domain and redirects: pass (No redirects off the domain)
- AI review of purpose and tool behavior: pass (No concerns)