# Forecast Accuracy & Track Record MCP (pypi · forecast-accuracy-mcp) Forecast track records: forecasts vs actuals, earnings beats, prediction markets, archived news. - Trust score: 77/100 (medium) - Registry status: active - Liveness: live - Owner verified: no - Last scored: 2026-11-05 ## Components - remote · `mcp.apify.com`: 48/100, [markdown](https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/mcp.md), [page](https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/mcp) - pypi · `forecast-accuracy-mcp`: 77/100 (this document), [markdown](https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/forecast-accuracy-mcp.md), [page](https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/forecast-accuracy-mcp) ## Channel facts - Registry: `pypi` - Package: `forecast-accuracy-mcp` - Version: `0.2.1` - Transport: `stdio` ## Trust breakdown How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog) Scored 2026-11-05. - **Supply Chain Security**: 100/100 - No malware found by supply-chain analysis. - No known CVEs affecting this package version or its production dependencies. - Runs hatchling.build at install time, a recognised build step with no custom scripting around it. - 1 of 28 dependencies flagged as unhealthy. - **Provenance & Transparency**: 97/100 - Source repository is publicly reachable at the declared URL. - Cryptographically verified build provenance (signed, bound to GeminiGeorge22/forecast-accuracy-mcp). - Clear OSI-approved license (MIT). - Actively maintained (last published 1 days ago). - Disclosure check failed: no security disclosure policy was found in the source repository. - **Schema Quality & AI Usability**: 53/100 - AI-judged instruction clarity (good). - Context-footprint check failed: tool/resource definitions use about 5080 tokens (~725/item across 7 items; 7 tools + 0 resources), over budget; trim descriptions and params. - Usage-examples check failed: none of the tools include examples. - **Stability & Change Management**: 0/100 - Stability not yet verified: not enough scan history yet (needs a 30-day window). - **Tool Coverage**: 100/100 - 100% of tools have a non-trivial description (not blank, and not just the tool's name). - 100% of tool parameters carry a description. - **Tool Safety**: 100/100 - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured. - We read all 7 captured tool definition(s), and no name or description among them implies an irreversible operation. - An AI judge read all 8 captured unit(s) of tool text and found none that tries to manipulate the model reading it. - **Capabilities**: 100/100 - Implements a current MCP spec version (2026-07-28). **Unverified: 1 category.** A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm. ## Install ### How do I install the Forecast Accuracy & Track Record MCP server? Forecast Accuracy & Track Record MCP runs locally as a PyPI package, launched with uvx forecast-accuracy-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation. ### Claude ```bash claude mcp add geminigeorge22-forecast-accuracy-mcp -- uvx forecast-accuracy-mcp ``` ### Cursor ```json { "mcpServers": { "geminigeorge22-forecast-accuracy-mcp": { "command": "uvx", "args": [ "forecast-accuracy-mcp" ] } } } ``` ### VS Code ```json { "servers": { "geminigeorge22-forecast-accuracy-mcp": { "command": "uvx", "args": [ "forecast-accuracy-mcp" ] } } } ``` ### Codex ```bash codex mcp add geminigeorge22-forecast-accuracy-mcp -- uvx forecast-accuracy-mcp ``` ### opencode ```json { "$schema": "https://opencode.ai/config.json", "mcp": { "geminigeorge22-forecast-accuracy-mcp": { "type": "local", "command": [ "uvx", "forecast-accuracy-mcp" ], "enabled": true } } } ``` ### OpenClaw ```bash openclaw mcp add geminigeorge22-forecast-accuracy-mcp --command uvx --arg forecast-accuracy-mcp ``` ### Hermes ```yaml mcp_servers: geminigeorge22-forecast-accuracy-mcp: command: "uvx" args: ["forecast-accuracy-mcp"] ``` ### Netclaw ```json { "McpServers": { "geminigeorge22-forecast-accuracy-mcp": { "Transport": "stdio", "Command": "uvx", "Arguments": [ "forecast-accuracy-mcp" ] } } } ``` ### Vellum ```bash assistant mcp add geminigeorge22-forecast-accuracy-mcp -t stdio -c uvx -a forecast-accuracy-mcp ``` ### Other ```json { "mcpServers": { "geminigeorge22-forecast-accuracy-mcp": { "command": "uvx", "args": [ "forecast-accuracy-mcp" ] } } } ``` ## Changelog Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims. ### 2026-11-04 (score 77, +15) - [security improvement] Malware scan: unverified → pass ### 2026-11-03 (score 62) First indexed and scored. ## MCP tools (7) ### `publicrecords--calibration-ledger` (~642 tokens) Calibration Ledger — trust a forecast, with evidence (beta) Calibration Ledger — trust a forecast, with evidence (beta). Given a claim from an organization (IMF, OECD, Fed/FOMC, SPF, ECB SPF, or a listed issuer), returns that organization's track record: error distribution, intervals, bias, band coverage, worst misses with source links — or honest 'no coverage' (free). Organizations only, never people. When to use: Use when someone cites a forecast from an institution (IMF, OECD, Fed/FOMC, SPF, ECB SPF or a listed issuer) and you need to know how accurate that institution's past forecasts were. Returns error distribution, bias and worst misses with source links, or an explicit no-coverage answer. Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "publicrecords/calibration-ledger" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/publicrecords/calibration-ledger Input parameters: - `band_kind` (string): For Fed SEP score mode: central_tendency or range. - `claim_id` (string): Ledger claim id for verify mode. - `claim_text` (string): Free-text claim for text mode (e.g. "The IMF expects Canada's economy to grow 1.8% in 2027"). Example: "The IMF expects Canada's economy to grow 1.8% in 2027." - `geo` (string): ISO3 country code, EA, or USA. Required for most score/lookup/compare calls. Example: "CAN" - `horizon_bucket` (string): Optional horizon bucket filter for score mode. - `issuer` (string): Organization name as it appears on its releases/filings (e.g. Salesforce Inc). Both records are returned: dated promises (newswires) and numeric guidance (EDGAR). - `mode` (string, required): text = hand it a sentence ("The IMF expects Canada's economy to grow 1.8% in 2027"). lookup/score/compare use structured fields. - `point` (string): Numeric claimed value for lookup mode (string so decimals stay exact). Example: "1.8" - `published_at` (string): When the claim was published (YYYY-MM-DD), for lookup/verify. Example: "2026-06-01" - `series` (string): Generated from rules.yaml — every series the ledger resolves. Example: "RGDP_GROWTH_A" - `source` (string): imf_weo | oecd_eo | fed_sep | spf | ecb_spf | pbo, or an issuer name; optional in text mode Example: "oecd_eo" - `target_period` (string): Target period as YYYY, YYYYQn, or YYYY-MM when looking up a specific claim. Example: "2027" ### `pintostudio--economic-calendar-data-investing-com` (~568 tokens) Economic Calendar Data (Investing.com) Economic Calendar Data (Investing.com). Extract structured economic calendar events (actual, forecast, previous, importance) from Investing.com across 45+ countries. Built for algorithmic trading systems, AI trading agents, RAG pipelines, and financial research. Filter by country, importance, category, and date range. When to use: Use when you need scheduled macro releases with actual vs forecast vs previous values (Investing.com economic calendar) by country, importance and date range. Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "pintostudio/economic-calendar-data-investing-com" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/pintostudio/economic-calendar-data-investing-com Input parameters: - `categories` (string): List of categories to which the events will be related to. If empty or null, all available categories will be taken into consideration. Example: "" - `countries` (array): Select one or more countries/regions. Combined with "Country" above. Leave empty (and Country blank) to include all countries. - `country` (string): A single country to retrieve economic calendar events for (e.g. United States, Euro Zone). To select several countries, use "Countries (multiple)" below. Leave both blank to include all countries. Ex… - `fromDate` (string): Start date for retrieving the economic calendar in 'YYYY-MM-DD' format. If not provided, only the current day's economic calendar will be retrieved. - `importances` (string): List of importance ratings for events. Can contain 'high', 'medium', and 'low'. If empty or null, all importance ratings including holidays will be considered. Options: high = High; medium = Medium;… - `timeFilter` (string): Determines how time is displayed: 'time_only' shows the event time, 'time_remain' shows remaining time until event. Default is 'time_only'. Options: time_only = Time Only; time_remain = Time Remainin… - `timeZone` (string): Time zone in GMT +/- hours:minutes format (e.g., 'GMT +2:00'). If not provided, the local GMT time zone will be used. - `toDate` (string): End date for retrieving the economic calendar in 'YYYY-MM-DD' format. If not provided, only the current day's economic calendar will be retrieved. ### `scrapemint--stock-earnings-estimates` (~458 tokens) Stock Earnings Estimates & Results: EPS Forecast vs Actual Stock Earnings Estimates & Results: EPS Forecast vs Actual. Keyless earnings data for any US stock from NASDAQ: what analysts expect the company to earn next quarter, what it actually earned in past quarters, the percentage it beat or missed by, how often it has beaten, and whether analysts raised or cut their forecast in the last four weeks. When to use: Use when you need analyst EPS estimates vs reported earnings for a US stock, beat/miss history and recent estimate revisions. Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "scrapemint/stock-earnings-estimates" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/scrapemint/stock-earnings-estimates Input parameters: - `includeAnnual` (boolean): Return full year estimates alongside the quarterly ones. - `maxRows` (integer): Cap on rows returned across all tickers. Controls total cost. - `minBeatRate` (integer): Keep only companies that beat expectations in at least this share of their reported quarters. 75 returns serial beaters. 0 = no filter. - `mode` (string): summary = one row per ticker: what analysts expect next quarter, what the company delivered last quarter, how often it has beaten, and whether estimates are being raised or cut. surprises = one row p… - `onlyRaisedEstimates` (boolean): Keep only tickers where more analysts raised their forecast than cut it in the last four weeks. Estimate revision direction is the forward looking half of this data. - `symbols` (array): US tickers to look up, e.g. NVDA, TSLA, AAPL. Maximum 200 per run. ### `fatihtahta--kalshi-scraper` (~344 tokens) Kalshi Scraper | Fast & Reliable Kalshi Scraper | Fast & Reliable. Get live prediction market data from Kalshi.com with tickers, categories, prices, and volume metrics. Fast, reliable, and structured for analysis. Ideal for traders, researchers, and data teams. When to use: Use when you need current Kalshi prediction-market prices, volumes and categories. Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "fatihtahta/kalshi-scraper" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/fatihtahta/kalshi-scraper Input parameters: - `maxResults` (integer): Limit how many market listings the actor should collect before stopping. - `pageSize` (integer): How many search results to fetch per API call. The actor caps this between 1 and 100. - `proxyConfiguration` (object): By default it uses Apify Residential proxies. - `searchQueries` (array): List the keywords you want to search for. - `startUrls` (array): Paste any Kalshi market or search result URLs that you want the actor to visit. - `targetUrl` (string): Paste one Kalshi search or market URL for a quick run. - `targetUrls` (array): Optional alias for startUrls. Paste more Kalshi search or market URLs here. ### `parsebird--polymarket-scraper` (~466 tokens) Polymarket Leaderboard Scraper Polymarket Leaderboard Scraper. Scrape Polymarket leaderboard rankings with full trader profiles. Extract wallet addresses, usernames, profit/loss, volume, positions, recent trades, bios, and social links. Filter by time range, category, and ranking type. When to use: Use when you need Polymarket leaderboard rankings and trader profiles (profit/loss, volume, positions). Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "parsebird/polymarket-scraper" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/parsebird/polymarket-scraper Input parameters: - `category` (string): Market category to filter the leaderboard. OVERALL includes all categories. Options: OVERALL = All Categories; POLITICS = Politics; SPORTS = Sports; CRYPTO = Crypto; FINANCE = Finance; CULTURE = Cult… - `includeActivity` (boolean): Fetch the last 10 trades for each trader. Adds trade details like market title, outcome, side (buy/sell), size, price, and timestamp. - `includePositions` (boolean): Fetch current open positions for each trader. Adds position details like market title, outcome, size, average price, current value, and PnL. - `leaderboardType` (string): Rank traders by profit/loss (PNL) or trading volume (VOL). Options: PNL = Profit / Loss; VOL = Volume. - `maxResults` (integer): Maximum number of traders to scrape from the leaderboard (1-1000). - `timeRange` (string): Leaderboard time period. DAY = today, WEEK = this week, MONTH = this month, ALL = all time. Options: DAY = Today; WEEK = This Week; MONTH = This Month; ALL = All Time. ### `ryanclinton--wayback-machine-search` (~1925 tokens) Wayback Machine Scraper - Track Website Changes Over Time Wayback Machine Scraper - Track Website Changes Over Time. Search the Internet Archive's Wayback Machine for historical snapshots of any website. Retrieve archived page metadata -- including timestamps, URLs, MIME types, HTTP status codes, and content hashes -- for up to 10,000 snapshots per run. When to use: Use when you need archived Wayback Machine snapshots of a URL or domain over time, e.g. to see what a page said on a past date. Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "ryanclinton/wayback-machine-search" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/ryanclinton/wayback-machine-search Input parameters: - `alertOnMagnitude` (string): Emit a single "alert" record at the top of the dataset when changes meet the threshold. Pairs well with scheduling: connect a webhook → Slack/email when the alert record fires. Requires Change Intell… - `autoPaginate` (boolean): When the CDX API hits its 10,000-row cap, automatically chunk the query year-by-year and merge the results — letting you pull full multi-year domain histories in one run. Costs more proxy time; only… - `changeIntelligence` (boolean): Augment every changed row with a changeSummary (magnitude: minor/moderate/major, categories: pricing/legal/product/layout/etc., keyDiffs: human-readable change phrases) and a structured diff (added/r… - `collapseBy` (string): Collapse duplicate snapshots. "digest" = unique content only, "timestamp:6" = one per month, "timestamp:8" = one per day, "timestamp:10" = one per hour Options: = No collapsing; digest = Unique cont… - `dateFrom` (string): Start date filter (YYYYMMDD or YYYY format, e.g., "20200101" or "2020") - `dateTo` (string): End date filter (YYYYMMDD or YYYY format, e.g., "20231231" or "2023") - `deltaSinceLastRun` (boolean): Turn on for stateful monitoring without external storage. The actor remembers the events and snapshot URLs returned in the previous run (in a named key-value store) and filters this run to only the e… - `detectChanges` (boolean): Compare each snapshot to the previous one and flag rows where the content hash or HTTP status changed. Adds a "changed" boolean and "changeType" string to every result. Free — uses metadata you alrea… - `fastLatest` (boolean): Return the most recent snapshots faster by skipping older index segments. Only useful for domains with hundreds of thousands of snapshots — combine with Max Results to fetch the latest N quickly. - `forceJsRender` (boolean): If `useJsRender` is on, render every snapshot via Playwright instead of falling back from a cheap HTTP fetch. Use when you already know your URLs are SPA shells and don't want to pay for the cheap-fe… - `generateReport` (boolean): Always write a REPORT.md to the run's key-value store, even when Output Mode is "snapshots". Useful when you want both raw rows and a human-readable summary. - `includeContent` (boolean): Fetch and include the archived page text content. Required for the diff/keyDiffs fields under Change Intelligence. Slow — fetches each snapshot individually with polite rate limiting. - `includeInsights` (boolean): Push a single "insights" record at the top of the dataset with key events, risk signals, business signals, typed events, coverage, cross-URL comparison, and chartData. Turn off if you only want raw r… - `matchType` (string): How to match the URL. "exact" = exact URL only, "prefix" = URL prefix match, "host" = all pages on same host, "domain" = all subdomains too Options: exact = Exact URL; prefix = URL Prefix; host = Sam… - `maxContentFetch` (integer): Maximum number of pages to fetch content for (only used if Include Page Content is enabled) - `maxResults` (integer): Maximum number of snapshots to return per URL (max 10,000 in a single CDX query — enable Auto-paginate to exceed). Ignored when Target Date is set (always returns one snapshot). - `mimeFilter` (string): MIME type filter (e.g., "text/html" for HTML pages only) - `monitor` (boolean): Turn on for scheduled monitoring runs. One flag enables: change detection, only-changed snapshots, change intelligence + diffs, alerts on moderate-or-major changes, timeline output, insights record,… - `monitorStateKey` (string): Optional override for the key-value-store key used by Delta mode. Auto-derived from the sorted URL list if not set; provide a stable string here if you want to share state across separate runs that h… - `onlyChanged` (boolean): Filter results to only the snapshots where the page actually changed (plus the first "initial" baseline). Requires Detect Changes. Turns 1,000 raw snapshots into ~50 actionable rows. - `outputMode` (string): What records to emit. "Snapshots" = one row per snapshot (default). "Timeline" = one row per stable version (consecutive identical-digest snapshots collapsed). "Report" = a markdown report in the key… - `proxyConfiguration` (object): Proxy used to reach the Wayback Machine CDX API and archived pages. Defaults to AUTO — Apify's smart router uses datacenter IPs where they work and falls back to residential where blocked. Switch to… - `query` (string): Filter the output to only snapshots whose change content matches a keyword. Searches changeSummary categories (e.g. "pricing"), keyDiffs, added/removed text, and full content. Pure post-filter — no e… - `statusFilter` (string): HTTP status code filter (e.g., "200" for successful pages only) - `targetDate` (string): Return the single snapshot closest to this date — useful for legal/compliance evidence ("what did the page say on 2020-06-15?"). Accepts YYYY-MM-DD or YYYYMMDD. When set, overrides Max Results and re… - `url` (string): URL or domain to search for snapshots (e.g., "example.com" or "https://example.com/page"). Ignored if you supply a list under URLs (batch). Console UI prefills "apify.com" as a placeholder; programma… - `urls` (array): Optional: query multiple URLs in one run for comparative competitive intelligence. Each URL gets its own snapshots, change detection, and metrics in the output. The single URL above is added if both… - `useCase` (string): Pick a preset to auto-configure filters, change detection, and output mode for a common workflow. Set to "custom" to use the raw inputs below. SEO = monthly buckets + 200-only + change-only. Complian… - `useJsRender` (boolean): Enable Playwright rendering for archived pages that return as empty React/Next/Gatsby SPA shells (notably HubSpot, late-2024+ Atlassian). Off (default) uses a cheap HTTP fetch only — fastest and chea… ### `data_xplorer--google-news-scraper-fast` (~633 tokens) Google News Scraper Google News Scraper. Scrape Google News in real time, including images and descriptions. This tool extracts complete structured data: high-resolution visuals, full, titles, sources, dates, and direct URLs. When to use: Use when you need recent Google News articles for a query (title, source, date, URL, image). Returns: the run's dataset rows as structured JSON (one row per result), plus the run and dataset IDs for paging the full dataset. Behavior: starts one run of the Apify Actor "data_xplorer/google-news-scraper-fast" through Apify's hosted MCP gateway (mcp.apify.com) with your own APIFY_TOKEN; the run uses your Apify account. Read-only: it only reads public web data and changes nothing on the source site. Docs: https://apify.com/data_xplorer/google-news-scraper-fast Input parameters: - `decodeUrls` (boolean): If enabled, decodes Google News URLs to get the original article URLs. Note: This will significantly slow down the scraping process as each URL needs to be decoded individually. - `extractDescriptions` (boolean): If enabled, extracts the meta description from each article page. Note: This will significantly slow down the scraping process. - `extractImages` (boolean): Fetches the og:image from each article page when no image is available (mainly for RSS/topic results). When this option is not explicitly set, images are extracted by default. Toggle OFF to skip imag… - `keywords` (array): List of keywords to search for. You can use Google News search operators: - Use '-' to exclude words (e.g. 'bitcoin -ethereum') - Use 'site:' to filter by specific website (e.g. 'bitcoin site:cnn.com… - `maxArticles` (integer): Maximum number of news to extract per keyword or topic (0 = no limit) - `proxyConfiguration` (object): Configure Apify Proxy. Residential proxies recommended for best results. - `region_language` (string): Region and language for news search - `timeframe` (string): Time period for the news search. Note: this filter applies mainly to keyword searches. Topic pages return their own curated results. Options: 1h = Last hour; 1d = Last day; 7d = Last week; 30d = Last… - `topicUrls` (array): Provide custom Google News topic or section URLs to scrape. Useful for niche topics not in the predefined list. Example: https://news.google.com/topics/CAAqJggKIiBDQkFTRWdvSUwyMHZNRGx1YlY4U0FtVnVHZ0p… - `topics` (array): Select one or more predefined Google News topics to scrape. ## Diagnostics Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/forecast-accuracy-mcp#diagnostics ## Score history - 2026-11-05: 77 - 2026-11-04: 77 - 2026-11-03: 62 ## Common questions ### What is the Forecast Accuracy & Track Record MCP server? Forecast Accuracy & Track Record MCP is listed in the public MCP registry as io.github.GeminiGeorge22/forecast-accuracy-mcp. Forecast track records: forecasts vs actuals, earnings beats, prediction markets, archived news. This page covers its PyPI package (forecast-accuracy-mcp). ### Is the Forecast Accuracy & Track Record MCP server safe to use? Forecast Accuracy & Track Record MCP scores 77 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm. ### What tools does the Forecast Accuracy & Track Record MCP server expose? Forecast Accuracy & Track Record MCP exposes 7 tools: publicrecords--calibration-ledger, pintostudio--economic-calendar-data-investing-com, scrapemint--stock-earnings-estimates, fatihtahta--kalshi-scraper, parsebird--polymarket-scraper, and 2 more. Their descriptions and schemas cost roughly 5,036 tokens of context every time the server is loaded. ### Is the Forecast Accuracy & Track Record MCP server still maintained? Forecast Accuracy & Track Record MCP is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced. ### What licence is the Forecast Accuracy & Track Record MCP server under? Forecast Accuracy & Track Record MCP declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls. ## Links - PyPI project: https://pypi.org/project/forecast-accuracy-mcp/ - Socket report: https://socket.dev/pypi/package/forecast-accuracy-mcp - Repository: https://github.com/GeminiGeorge22/forecast-accuracy-mcp - Changelog RSS feed: https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/forecast-accuracy-mcp.xml - Changelog JSON feed: https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/forecast-accuracy-mcp.json - HTML version of this page: https://verifymcp.io/servers/geminigeorge22-forecast-accuracy-mcp/forecast-accuracy-mcp