Finance Research MCP

Tools 61

• FinanceMCP_ai_chain_profile Where one company sits in the AI value chain of 'US Market Fault Lines' (Links 1-6: lab returns, lab funding, backlog cash, builders' capex and credit, supplier and index earnings, funders' marks) and how exposed it is: role (graph edges and theme segments), capex against operating cash flow and FCF, exposure to the labs (INVESTED_IN rounds, named customer or supplier links with quotes), backlog (RPO) where tagged, financing (maturity ladder, GPU-collateralised 8-K facilities, lenders, hand-collected financing events), share of S&P 500 forward earnings if an AI supplier, disclosed AI or data-centre revenue, the section-10 tripwires that bear on it, and AI hiring. A company the warehouse does not place in the chain is reported as such, with the same cash and exposure sections. Args: ticker: ticker (share classes too), CIK or company name; resolve_company shows how it was read detail: concise (headline values, about 3-4k tokens) | standard (tables) | full (definitions, accession
• FinanceMCP_ai_cycle The AI-investment cycle against the causal chain in 'US Market Fault Lines' (Links 1-6, section-10 tripwires). view: tripwires (the ten signals computed from the warehouse, with tripped flags and the contradiction test), capex (builders' quarterly capex, OCF, FCF, capex/OCF, RPO; tickers default MSFT, GOOGL, AMZN, META, ORCL, CRWV, NBIS), concentration (AI suppliers' share of S&P 500 forward earnings), private_credit (BDC exposure to software, IT services, AI infrastructure), refinancing (debt ladders of the builders and power names), or all. Args: view: Value for view. Allowed values: ['all', 'tripwires', 'capex', 'concentration', 'private_credit', 'refinancing']. tickers: Value for tickers. quarters: Value for quarters. top_groups: Value for top_groups. detail: concise (headline values, about 3-4k tokens) | standard (tables) | full (definitions, accessions, longer tables) Allowed values: ['concise', 'standard', 'full'].
• FinanceMCP_business_profile What the company actually DOES: revenue mix (product / geography / segment), operating KPIs (store counts, ARPU, GRM, capacity utilisation, etc.), market share and capex allocation by segment. The starting point for a business/moat/SWOT study. Args: symbol: Value for symbol.
• FinanceMCP_capital_allocation How management deploys capital over time: operating cash flow vs capex vs free cash flow vs net debt vs dividend payout. Answers 'is growth self-funded or debt-fuelled, and does management return cash' — central to judging stewardship/trustworthiness. Returns a 2D per-year trend chart (PNG) alongside the data when at least 2 years of cash-flow history are available. Args: symbol: Value for symbol.
• FinanceMCP_company_documents List the filings available RIGHT NOW for a company: concall transcripts and presentations, annual reports, credit-rating rationales and recent announcements, each with a date and a URL. Use this instead of search_documents when you want a company's own words — then call read_document on the ones you need. Works for any listed Indian company without a prebuilt index. Args: symbol: Value for symbol. kind: Value for kind. Allowed values: ['all', 'concalls', 'annual_reports', 'credit_ratings', 'announcements']. limit: Value for limit.
• FinanceMCP_company_dossier One US-listed company in depth, assembled from the warehouse. sections (comma list; default all): identity, fundamentals (TTM, annual, quarterly, balance sheet, employees), quality (Piotroski, Altman, accruals, ROIC), capital_allocation, growth (CAGRs, base rate for the growth the price implies), valuation (multiples, WACC, reverse DCF), street (consensus targets, ratings, EPS revisions, estimates), guidance (history and guidance versus delivery), governance (CEO pay, pay versus performance, tenure, 13D/G), insiders (open-market buys and sells, recent Form 4 trades), people (officers and directors, leadership changes, H-1B role mix, job postings, layoffs), moat, ip (R&D, patents to 2021, Orange Book), debt (leverage, maturity ladder, recent financings), distress (going-concern and weakness flags), positioning (short interest, borrow, options), events (material 8-Ks), supply_chain (named customers and suppliers at confidence >= min_confidence, disclosed concentration), segments, documen
• FinanceMCP_company_overview Company profile card: about, sector, key metrics (mcap, PE, ROE/ROCE, sales/PAT), ownership split, index memberships with weights, peers, and available data inventory. Args: symbol: Value for symbol.
• FinanceMCP_company_valuation What the price implies and what a scenario gives, for one US company. mode='reverse' (default): multiples (P/E, EV/EBIT, EV/EBITDA, EV/sales, P/B, FCF yield), WACC (CAPM with Damodaran implied ERP, own 5y weekly beta) and the constant FCF growth over `years` that the current EV implies, on both FCF bases, with a sensitivity to the discount rate and terminal growth. mode='scenario': two-stage FCF-after-SBC DCF, growth fading linearly from g_start to g_end over growth_years, terminal growth scenario_terminal_growth; every assumption echoed, with a 3x3 sensitivity. mode='all' runs both. discount_rate=0 uses the computed WACC. basis 'reported' (OCF - capex - SBC) or 'maintenance' (OCF - min(D&A, capex) - SBC, for heavy growth-capex years). fcf_base (USD) overrides the TTM base in a scenario when TTM FCF is negative. Outputs are arithmetic on stated inputs, not forecasts or recommendations. Args: ticker: ticker (share classes too), CIK or company name; resolve_company shows how it was
• FinanceMCP_competitive_position Competitive standing: head-to-head benchmarking vs named peers on operating metrics, market-share trend, and the peer set. Use for 'how does X stack up against competitors' and as the competition input to a SWOT. Args: symbol: Value for symbol.
• FinanceMCP_country_risk Damodaran country risk for one country: Moody's, S&P and Fitch sovereign ratings, rating-based default spread, 10-year sovereign CDS (and net of the US/Swiss CDS), country risk premium, total equity risk premium (and the CDS-based variants), mature-market and US ERP, corporate marginal tax rate, GDP and PRS score - from the latest update held (July 2026 at present) and the April 2026 update, plus a history table across every update in the warehouse (January 2026, July 2025, ...) and the latest monthly implied ERP of the S&P 500. country: a name ('United States', 'Taiwan', 'India', 'Germany'; US/UK/UAE accepted) or '?' to list them. Args: country: country name, or '?' to list detail: concise (headline values) | standard (every curated metric / longer tables) | full (every column of every file; get_series up to 5,000 points) Allowed values: ['concise', 'standard', 'full'].
• FinanceMCP_customers_of Named customers of a company with verbatim quotes, source, date, confidence, products and revenue share where stated (same fields as suppliers_of). Anonymised customers ('Customer A accounted for 22%') are in doc_facts(fact_type='customer_concentration'); federal-government customers appear with node org:us-federal-government when an XBRL fact or sentence names them. Args: ticker: ticker (share classes too), CIK or company name; resolve_company shows how it was read min_confidence: Value for min_confidence. limit: Value for limit. detail: Value for detail. Allowed values: ['concise', 'full']. include_unchecked: Value for include_unchecked.
• FinanceMCP_demand_signals AI demand signals: quote-backed statements from the earnings calls (last 8) and 10-Q MD&A (last 6) of the AI-chain companies about customer orders, backlog, capex plans, supply constraints and inventory, each with company, speaker, customer type (hyperscaler, neocloud, enterprise, sovereign, china, consumer), named customer, product, direction, stated magnitude and period, the verbatim quote, date and chunk_id (doc_read(chunk_id) shows the surrounding text). Use it for 'are customers cutting or pausing AI spend', 'who reports pull-ins or push-outs' before searching raw text. ticker: one or a comma list. theme: ai-chain node (labs_and_apps, cloud_builders, data_centers, accelerators, memory, foundry_packaging, equipment, networking_optics, servers, power, fuel) or 'negative' (decrease, pause_digestion, push_out, cancellation). direction: increase, decrease, pause_digestion, push_out, pull_in, cancellation, steady or 'negative'. signal_type: customer_demand, own_capex, supply_constraint,

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