Integration details
Description
Lexfi gives ChatGPT access to structured financial data for research and analysis. It covers equities, macroeconomics, fixed income, institutional holdings, market sentiment, alternative data, and global markets through a unified MCP connection. Users can retrieve market data, company information, economic indicators, ownership activity, and financial signals directly in conversation. Lexfi is a financial data provider and does not execute trades or provide investment advice.
- Integration type
- Plugin
- Verification status
- Not applicable
- Platform
- ChatGPT
- Category
- Pending
- Primary Subcategory
- Pending
- Secondary Subcategories
- None listed
- Brand
- Unknown
- Access
- Account required
- First tracked
- 2026-09-13
- Tool count
- 139
- Geography
- US
The broad Category that contains the Primary Subcategory.
The Primary Subcategory used for this profile’s headline score.
Other Subcategories where the Integration is listed.
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What discovery looks like
Competitive lineup
139 tools agents can invoke
Get AI_FX_ratio time-series for a currency. Example: get_ai_fx_ratio({ currency: 'USD', lookbackDays: 30 }).
Active weather alerts for a US state. Use for immediate hazard checks before travel or operations decisions. Keywords: weather alerts, severe weather, warnings, advisories, state alerts.
Forward analyst consensus estimates (EPS, revenue, EBITDA, net income). Use before earnings to evaluate the market hurdle and expected results. Keywords: consensus, estimates, forecast, earnings preview, EPS expectations, revenue expectations.
Analyst ratings and target-price consensus (average, high, low, median; buy-hold-sell mix). Use for street sentiment and external positioning checks. Keywords: analyst ratings, target price, buy hold sell, Wall Street view, consensus rating.
Upcoming central-bank meeting calendar. Use for event scheduling, catalyst planning, and policy-risk timing context. Keywords: central bank calendar, meeting dates, policy events, macro calendar.
Verbatim central-bank press conference transcript from BigQuery `Transcripts_Luca.transcripts_job`. Provide bankId plus date (YYYY-MM-DD) or exact video_path from get_cb_insights. Returns title, date, video_path, and flat `text` (BQ stores a single transcription blob without speaker attribution). Use to verify AI summaries against source material. Keywords: central bank transcript, FOMC verbatim, ECB press conference text, video_path.
Structured insights extracted from central-bank press conferences, filterable by bank and date window. Returns a payload with bank metadata, a `summary` block (latestClassification, trend, conferenceCount, and averages over a configurable rolling window for the 11 numeric indices: hawkish_dovish_index, conference_sentiment, monetary_policy_uncertainty, geopolitical_uncertainty, inflation_concerns_index, labor_market_tightness_index, forward_guidance_clarity_index, and the four monetary_authorities_perceived_* fields), and a `conferences[]` array with per-conference rows (numeric columns as decimal strings, conference_highlights and monetary_policy_concerns as JSON-encoded strings). bankId must be a 3-4 letter CB code (e.g. fed, ecb, boe, boj, pboc, bok, rba), not a full bank name. Set includeTranscript=true to attach verbatim transcript text per conference (joined on bank + video_path). Use for hawkish-versus-dovish tone-shift interpretation. Keywords: central bank insights, hawkish, dovish, policy communication, FOMC, ECB press conference, guidance, monetary policy.
Top chain TVL leaderboard with dominance and recent change metrics. Use for ecosystem-relative strength and capital rotation across chains. Keywords: TVL, chain dominance, DeFi flows, ecosystem strength, capital rotation.
CMC100 index constituents and weights for top crypto assets by market cap. Use for benchmark composition and broad-market exposure analysis. Keywords: CMC100, index constituents, benchmark, broad crypto exposure.
CNN Fear and Greed index for equity-market sentiment, including headline score and components. Use for sentiment-regime checks and contrarian risk framing. Keywords: fear and greed, sentiment, risk appetite, contrarian, market psychology.
Historical market chart series for a cryptocurrency (price, market cap, volume over time). Use for period-performance and adoption-context analysis across days or weeks. Keywords: market chart, historical price, market cap history, volume trend.
Market data table for many coins (price, market cap, volume, 24h move), with pagination and optional category filters. Use for breadth scans and 'what is moving in crypto today'. Keywords: coin screener, movers, breadth, stablecoins, DeFi, meme coins.
OHLCV candlestick data for a specific cryptocurrency using coin IDs (e.g. bitcoin, ethereum, solana). Use for technical trend, volatility, and support or resistance analysis on a single coin. Keywords: OHLCV, candles, chart, technical analysis, volatility, coin trend.
Detailed executive pay breakdown (salary, bonus, stock, total) with industry compensation benchmark for peer context. Use for pay governance, alignment, and 'is this CEO overpaid' prompts. Keywords: executive compensation, CEO pay, salary, stock awards, benchmark, peer pay.
C-suite and key leadership roster with titles, tenure, and pay summaries. Use for management team and governance context before deeper analysis. Keywords: executives, leadership, CEO, CFO, management team, C-suite.
Company profile and identity data (sector, industry, CEO, IPO date, business description). Use when users ask 'what does this company do' or need context before deeper analysis. Keywords: profile, sector, industry, CEO, company overview, business.
Legacy earnings/conference call search (BigQuery alltranscripts) by company name and/or company_id, with optional date filters. MCP returns formatted text: transcript_id, title, timestamp, event, and text_blocks count — not full dialogue or structured insights. Requires at least one of companyName or companyId; limit 1–20 (default 10). Prefer get_earnings_calls_by_ticker when you have a ticker (alltranscripts_v2). For AI/ESG/metrics, use get_earnings_call_insights with a transcript_id from the ticker listing. Keywords: transcripts, company name, legacy, conference call.
House and Senate trade disclosures with optional chamber and ticker filters. Use for policy-linked trade flow checks and political trading narratives. Keywords: congress trading, senate trades, house trades, political disclosures, policy risk.
Country-level macro indicators. Use for country comparison, sovereign-risk framing, and structural macro context. Keywords: country metrics, GDP per country, inflation by country, sovereign risk. Only use this tool for getting historical data, if you want the latest data, this tool is not up to date.
Crypto prediction market rows from Futuur ranked by volume. Use for Futuur-specific event-odds retrieval and venue comparison. Keywords: Futuur, crypto prediction markets, event odds, probability.
Global crypto market snapshot (total market cap, 24h volume, BTC and ETH dominance, cap change). Use for top-down crypto regime and risk-on or risk-off context. Keywords: crypto market overview, dominance, total cap, risk sentiment, macro crypto.
Crypto prediction market rows from Kalshi ranked by volume. Use for Kalshi-specific implied probabilities and cross-venue comparisons. Keywords: Kalshi, crypto odds, event contracts, probability pricing.
Daily crypto-news sentiment counts (aggregate or symbol-level) across configurable date windows. Use for narrative trend and sentiment-divergence analysis. Keywords: crypto sentiment, news tone, positive negative, narrative shift, lookback. If you want to fetch by symbol, use the symbol parameter as XXXUSD.
Crypto prediction market rows from Polymarket ranked by volume. Use for Polymarket-specific odds and event-pricing checks. Keywords: Polymarket, crypto event odds, implied probability, prediction contracts.
Aggregated crypto prediction market opportunities ranked by activity and volume. Use when users ask about probabilities, odds, or chances of crypto events. Keywords: prediction markets, crypto odds, probability, chance, implied likelihood.
Latest curated crypto highlights from Reddit. Use for community narratives, meme rotations, and grassroots theme discovery. Keywords: Reddit crypto, community sentiment, meme narrative, forum trends.
Latest curated crypto highlights from Stocktwits. Use for retail sentiment and crowd discussion pulse checks. Keywords: Stocktwits, retail crypto sentiment, social buzz, crowd chatter.
Latest curated crypto highlights from X and KOL sources. Use for fast narrative detection and social attention flow. Keywords: X highlights, KOL, crypto chatter, social narrative, attention.
Current Argentine market rates/prices. REQUIRED dataset. Rates are decimal fractions (0.345 = 34.5% annualized for cauciones). Always check `warning` and `freshness` in the response — empty data with a warning is valid after hours/weekends. For composite sovereign + letras use dataset=titulos_publicos. For caución plaza and daily CPD/FCE/pagaré volumes use get_macro_merval_dashboard. Proxies GET /internal/market-data/current.
Get daily commodity (or related) history using a symbol such as BZUSD, NGUSD, HGUSD, ALIUSD, GLD, SLV, or WTI, with optional startDate and endDate (YYYY-MM-DD). `changePercent` is returned as a DECIMAL (0.0123 = +1.23%) — multiply by 100 for display.
Get daily DXY index history, with optional startDate and endDate (YYYY-MM-DD). `changePercent` is returned as a DECIMAL (0.0123 = +1.23%) — multiply by 100 for display.
Get daily FX history for local currency vs USD pairs such as USDBRL. Input supports symbol plus optional startDate and endDate (YYYY-MM-DD).
Get daily FX pair history for any supported pair symbol (e.g. EURUSD, GBPUSD, USDJPY), with optional startDate and endDate (YYYY-MM-DD).
Get daily S&P 500 index history using symbol ^GSPC, with optional startDate and endDate (YYYY-MM-DD).
Get daily VIX index history using symbol ^VIX, with optional startDate and endDate (YYYY-MM-DD). `changePercent` is returned as a DECIMAL (0.0123 = +1.23%) — multiply by 100 for display.
Structured insight bundle for one earnings call across six insight tables (ais, communication, digital_strategy, esg_sentiment, esg_topic_mix, metrics) keyed by transcript_id from get_earnings_calls_by_ticker. Returns JSON-in-text inline — do not fetch REST URLs. Required: ticker (validates company match) and transcriptId. Default: all six insight tables; no transcript row. Use tables to subset (e.g. ['metrics']). includeTranscript=true adds the full transcript row (metadata + verbatim text_blocks; large payload). May return warnings on ticker/transcript mismatch or per-table errors while still returning partial data. Keywords: earnings call insights, sentiment, guidance, ESG, AI summary, metrics, verbatim quote.
List earnings/conference calls for a ticker. MCP returns formatted text with transcript_id, title, timestamp, event_type, and period — no speakers list and no text_blocks. Default limit 25 (max 1000); optional startDate/endDate. Step 1 before get_earnings_call_insights: copy transcript_id from the row you want. Keywords: earnings calls, transcript id, ticker, NVDA, AAPL, list calls, latest call.
Quarterly earnings surprise history (estimated EPS versus actual EPS). Use to assess beat or miss consistency and reporting execution over time. Keywords: earnings surprise, beat, miss, EPS actual, EPS estimate, quarterly results.
Get economic calendar events for a date window. Inputs are optional startDate and endDate in YYYY-MM-DD format.
Latest disclosed workforce size plus historical headcount by reporting period. Use for 'how many employees' questions and growth, layoff, or scaling analysis. Keywords: employee count, headcount, workforce, staffing, FTE, hiring, layoffs.
ETF AUM and product-level metrics for BTC and ETH spot or futures ETFs (fund size, price, volume, fees). Use for product-share and adoption traction checks. Keywords: ETF AUM, fund size, product share, adoption, ETF metrics.
Daily spot ETF flow data for BTC, ETH, and SOL products, including net and cumulative flows. Use for institutional demand and allocation trend analysis. Keywords: ETF flows, net inflow, net outflow, institutional demand, spot ETF.
ETF constituent holdings with symbols, names, weights, and shares. Use for exposure decomposition like 'what is inside QQQ or XLE'. Keywords: ETF holdings, constituents, weights, basket exposure, portfolio composition.
ETF metadata (expense ratio, AUM, issuer) plus sector and country allocation weights. Use for fund comparison and geographic/sector exposure context. Keywords: ETF info, expense ratio, sector weights, country allocation, fund profile.
Crypto Fear and Greed index history with 0 to 100 regime labels. Use for sentiment-extreme and contrarian framing prompts. Keywords: fear and greed, sentiment regime, extreme fear, extreme greed, contrarian.
Financial statements for a company (income statement, balance sheet, cash flow; quarterly or annual). Use for deep fundamental analysis of margins, leverage, cash generation, and accounting quality. Keywords: financials, income statement, balance sheet, cash flow, quarterly, annual.
Point weather forecast for a location by latitude and longitude. Use for location-specific weather outlook and planning. Keywords: forecast, weather outlook, local weather, temperature, precipitation.
Daily forex-news sentiment counts (positive, neutral, negative), aggregate or pair-specific with date filters. Use for FX narrative direction and tone shifts. Keywords: forex sentiment, FX news tone, pair sentiment, narrative shift.
Official fund/ETF disclosure filings: available reporting dates and N-PORT style holdings for a fund symbol. Use for regulatory holdings snapshots and filing-period analysis. Keywords: fund disclosure, N-PORT, mutual fund holdings, ETF filing.
Perpetual funding rates across exchanges with coin-level and venue-level breakdowns. Use for long versus short positioning imbalance and carry conditions. Keywords: funding rates, perp funding, long bias, short bias, carry trade.
How do I improve a ChatGPT Plugin's discoverability?
The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.
Where is this profile measured?
This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.