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205,011 tools. Last updated 2026-06-15 02:11
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- Use this tool when the user asks BOTH what a financial figure is AND which filing reported it — for example "What was Apple's most recently reported revenue, and which 10-Q filed it?" or "Show me the accession ID for Tesla's latest net income" or "Which filing form reported Amazon's Q3 operating cash flow?" This tool returns a single fact plus its complete filing provenance: entity, concept, period, value, accession ID, filing URL, and form type (10-K, 10-Q, etc.). Use this INSTEAD OF `search_companies` when the user already names a company and wants a financial figure with its source filing — `search_companies` only resolves company identifiers and returns no financial data. Use this INSTEAD OF `get_company_fundamentals` when the user explicitly wants to know which filing or form type reported a number, or needs the accession ID — `get_company_fundamentals` returns metrics across multiple periods but omits filing provenance. Two lookup modes: (1) by fact_id (SHA-256 hash of entity_id|accession_id|concept|period_end|unit) for deterministic identity; or (2) by concept name (e.g., TotalRevenue, NetIncome, EPSDiluted, TotalAssets, OperatingCashFlow) plus a ticker to retrieve the most recently reported fact. Optionally pin a point-in-time cutoff via as_of_date (YYYY-MM-DD) — returns the latest filing accepted by SEC on or before that date, eliminating look-ahead bias. Check `_meta.pit_safe` in the response to confirm PIT correctness. DURATION: income-statement flow concepts (NetIncome, TotalRevenue, etc.) are reported over a window, and a single 10-K tags BOTH a 12-month figure and a 3-month Q4 stub at the same fiscal-year-end period_end. On a tie this tool returns the longer (headline) window, and every result carries `period_type` (instant | quarterly | half_year | nine_month | annual | duration) and `period_span_days` so you always know whether a number is a quarter or a full year — never present a 3-month stub as the annual figure. Provide either fact_id or concept (required). Returns empty result with error_code FACT_NOT_FOUND if no matching fact exists for the given concept and ticker. Available on all plans.Connector
- List all 26 bundled reference templates in the Axint SDK. Returns a JSON array of { id, name, description } objects — one per template. Templates cover messaging, productivity, health, finance, commerce, media, navigation, smart-home, and entity/query patterns. No input... Use: use to discover valid template ids before templates.get. Effects: read-only template metadata; writes no files and uses no network.Connector
- Get **TOTAL** token flows per segment: 1. Public Figures 2. Top PnL Traders 3. Whales 4. Smart Traders 5. Exchanges 6. Fresh Wallets Inflow and outflow of tokens between the segments is CRITICAL in identifying token price trends. The values provided are **aggregated over the specific lookback period (last 5min, 1d, 7d etc) specified**. If you have SPECIFIC date ranges in mind, use `token_flows` instead. **NOTE** Use `token_flows` for more granular data as it can filter between exact dates and provides HOURLY breakdowns. Returns: Categorized token flow analysis as markdown. For each segment, returns: - Flow amount in USD - Ratio compared to average flow - Number of wallets Format: "{Segment} wallet flow of {amount} ({ratio}x average, from {count} wallets)" Notes: - Positive flow = net buying, negative flow = net selling - For Exchange Flow, positive means more inflow to exchanges, negative means more outflow from exchanges - Categorizes market participants by their historical behavior and characteristics NOTE: Bitcoin is not supported. DO NOT use this tool for bitcoin. **Modes:** - `onchain_tokens` (default): On-chain token flow intelligence across cohorts - `perps`: Hyperliquid perpetual futures — returns position intelligence (current aggregate long/short/total USD by cohort: Smart Money, Whales, Public Figures). Native tokens (SOL, ETH, BTC etc) are fully supported in perps mode.Connector
- Use this tool to answer questions about historical index membership — e.g. "Was Company X in the S&P 500 on date Y?" or "Which companies were in the Russell 2000 on 2010-01-01?" Use this INSTEAD OF `search_companies` when the question involves a specific historical date or asks whether a company was an index member at a point in the past. `search_companies` only returns current membership snapshots and cannot answer historical membership questions. Returns a survivorship-free universe of companies valid on a specific as_of_date: only companies that existed and were index members on that exact date — no hindsight contamination. Supports SP500, RUSSELL1000, RUSSELL2000, RUSSELL3000 via index_membership.parquet (accurate join/leave dates with [) interval semantics). To check a single company's membership, pass its ticker and the target date; if the company appears in the response it was a member, if absent it was not. Returns per company: CIK, ticker, name, sector, industry, SIC code, plus per-row membership confidence (high/medium/low). Check `_meta.pit_safe`: true only when every matched row is high-confidence; medium/low rows downgrade it to false — treat low-confidence rows with caution for backtest use. NOTE: `sector` is SIC-derived (GICS-aligned labels via sic_to_sector.csv), not licensed GICS — industrial conglomerates may map differently. Treat as a screening bucket, not an authoritative GICS label. Use as the first step of a quantitative backtest before calling `get_compute_ready_stream` to pull Parquet data for the universe. Returns empty array (with error detail) if the date is out of range or the index_membership data has no coverage for that date. Available on every plan — sample tier returns the subset covered by the sample bucket.Connector
- Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use search.files / search.threads / search.links for that.Connector
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- AlicenseAqualityDmaintenanceSmart Search MCP is an intelligent search toolkit focused on the technology field, providing 14 enhanced search tools covering mainstream international and domestic technology platforms. It has functions such as intelligent URL generation, input verification, and advanced search techniques, making iLast updated142Apache 2.0
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E-commerce competitor intelligence, VOC sentiment, Amazon ASIN analysis, Reddit growth playbooks.
Analyze job listings against your resume, track applications, and generate cover letters.
- Routes a prompt to the best available x711 LLM. No API keys, no rate limits. Use ONLY when you need external LLM help. Never for things you can answer from context. prefer options: - cheap = fastest + cheapest (classification, extraction) - fast = low latency - smart (default) = best reasoning / code Returns: { text: string, model: string, tokens_used: number, prefer: string }Connector
- Persist a directional investment thesis (bull / bear / neutral) on a ticker. The thesis becomes part of the caller's private research diary; pair with `list_theses` + `score_thesis_outcome` to track conviction-vs-outcome over time. Pass `idempotency_key` for at-most-once semantics from a retrying agent. **Use this AFTER** the agent has finished its analysis, not before — the thesis records the conclusion, not the question. Pair with `source_report_id` to link the thesis back to a published report so the buyer's thesis-tracking carries provenance. Tier: all paid + free tiers (sample tier rejected — sample is guest access with no customerId binding). Per-tier cap on # of stored theses: sp500=100, pro=500, full=10,000.Connector
- STATUS: pending — direct R2 Parquet access is in private beta (ETA 2026-Q3). Calls return 501 FEATURE_NOT_AVAILABLE today. When live: returns a pre-signed Cloudflare R2 URL for bulk Parquet access that can be piped into Python/DuckDB/Polars for high-throughput computation that exceeds the MCP context window. Datasets: fact (per-entity partition — requires ticker), ratio (all computed ratios), valuation (DCF inputs), filing (SEC filing metadata), references (company universe), index_membership (historical index composition). URL would expire in 15 minutes. TODAY use the Python SDK (`pip install valuein-sdk`) for the same data via DuckDB.Connector
- Compare a company's core financial metrics across two fiscal periods side-by-side. Shows absolute and percentage changes with significance classification (minor < 5%, notable 5–15%, significant > 15%). The response includes a `material_changes` count: this is the number of metrics whose `significance` ∈ {notable, significant} (i.e. absolute percentage change > 5%). Use it as a quick scalar to triage filings — anything > ~3 typically signals a material event worth deeper review. Use period format: 'FY2024' for annual, 'Q1-2024' for quarterly. Pass `period_a` as the EARLIER period and `period_b` as the LATER one — if you invert them the server auto-swaps and sets `swapped: true` in the response so deltas always carry the correct sign (rather than silently flipping). Point-in-time safe via as_of_date. Available on all plans.Connector
- Activate your agent's PingShield — a reputation-gated inbox that blocks low-rep senders from reaching you via x711_agent_ping. Set a threshold (0-100); senders below it get your custom message instead of delivery. Add a whitelist (always let through) or blacklist (always blocked). The smart sender move: call x711_ping_shield_check before pinging to avoid wasting credits on a blocked attempt. Requires API key. Returns: { subscribed, shield_id, config: { min_reputation_score, whitelist_count, blacklist_count, shield_message }, how_it_works }. Cost: $0.10.Connector
- Lists all published agentView store categories (e.g. Gastronomie, Wartezimmer, Empfang, Smart Home) with localized titles, descriptions and template counts. Use this to narrow a subsequent search_store_templates call when the user asks for 'templates for a waiting room' or similar. No authentication required. Returns count, language and a categories array where each entry has slug, title, description, templateCount, heroIconKey and detailPath.Connector
- Get direct links to original SEC EDGAR filings for any US public company. Returns two per-filing deep links: `sec_url` (the EDGAR filing-index page listing every document) and `viewer_url` (the SEC iXBRL inline-viewer for the specific accession). Supported form_types (enum): 10-K, 10-Q, 8-K, 20-F, 40-F, 10-K/A, 10-Q/A, 20-F/A, 40-F/A. Other forms (6-K, DEF 14A, Form 4, 13F) are NOT yet exposed by this tool — use `describe_schema` to confirm the parquet has them, then read raw via the SDK. 8-K item codes are filterable via `event_types` (e.g. ['2.02'] for earnings, ['1.01'] for material agreements, ['5.02'] for officer changes). PIT-safe — filings are filtered by accepted_at, never by report_date alone. Use this *instead of* `verify_fact_lineage` when you want a list of filings; use `verify_fact_lineage` when you want one specific fact-to-filing trace. Available on all plans.Connector
- Semantic search over SEC 10-K / 10-Q narrative sections — Risk Factors, MD&A, Business, Legal Proceedings, Controls & Procedures, Footnotes. Phrase the query as a statement rather than a question for best recall (e.g. "companies with rising supply chain concentration risk" not "what companies have supply chain risk?"). Returns narrative passages (text, not numeric facts) ranked by semantic similarity, each with the source filing accession id + URL for citation; `score` is a [0,1] similarity, not a financial figure. For dollar/ratio figures use `get_company_fundamentals` / `get_financial_ratios`. Cached at the per-plan tier for 10 min.Connector
- Get a multi-year capital allocation breakdown for a US public company. Shows how management deploys cash across all six categories — capex, R&D, M&A, dividends, buybacks, and debt — plus pre-computed deployment ratios (% of operating cash flow) and over-distribution flags. Use this tool when the user asks: how does a company allocate capital, what's the buyback-vs-dividend mix, is the company over-distributing, is growth funded by R&D or M&A, what's the cash return ratio trend, or any 'where does the money go' question. Also use for owner-earnings analysis (Buffett-style) and reinvestment-rate analysis (Damodaran-style). Data sourced from annual 10-K filings (SEC EDGAR) — income statement (R&D), investing section (capex, M&A), financing section (dividends, buybacks, debt). All figures are point-in-time safe via the as_of_date parameter — no look-ahead bias. R&D semantics: R&D is included as a deployment category despite being an income-statement expense, because for knowledge-economy businesses (tech, pharma, industrials with heavy engineering) it represents the primary growth reinvestment vehicle and often dwarfs capex. R&D is already deducted before reaching operating cash flow, so `rd_pct_ocf` is INFORMATIONAL — it does not reduce OCF a second time. The `total_deployment_pct_ocf` field excludes R&D from its sum to preserve the cash-flow identity (OCF = capex + M&A + dividends + buybacks + debt repayment + change in cash). Flags object: pre-computed booleans for common analytical questions. Use `buybacks_exceed_fcf` to identify years a company returned more to shareholders via repurchases than it generated in free cash flow. Use `total_returns_exceed_fcf` for the stricter test (buybacks + dividends > FCF). Use `debt_funded_distribution` to distinguish over-distribution funded by leverage (typical industrials) from over-distribution funded by cash hoard (Apple 2018-2019 post-tax-reform repatriation). NOT yet included (separate roadmap items): `buyback_yield_implied` requires a price × shares market-cap series; equity-method investments and intangibles are excluded from `acquisitions_net` to keep M&A semantics tight (request `other_investing_outflows` if needed). Available on all plans.Connector
- Get pipeline-computed financial ratios from ratio.parquet. Served categories: profitability (margins, ROE, ROA, ROIC), liquidity (current ratio, quick ratio), leverage (D/E, interest coverage, net debt/EBITDA), efficiency (asset turnover, inventory days), per_share (EPS, BVPS, FCF/share), owner_earnings (Buffett FCF, owner yield), and the pipeline-emitted forensic, growth, and rank (cross-sectional *_sector_pctile) categories. NOT every category exists for every ticker — the exact set is data-driven; omit `categories` to get whatever this ticker has, or read the `available_categories` list returned in the CATEGORY_NOT_AVAILABLE envelope. valuation (EV/EBITDA, P/E, P/FCF) is DECLARED BUT NOT YET POPULATED for any ticker (price feed pending) — requesting it returns a CATEGORY_NOT_AVAILABLE envelope, never data. Includes TTM (trailing twelve months) rows alongside annual periods. Each row carries `is_calendar_aligned` (boolean) — TRUE when period_end is actually on the entity's fiscal year boundary (±7 days), FALSE when the pipeline emitted a calendar-quarter row tagged fiscal_period='FY' for a non-December fiscal-year filer. Filter to `is_calendar_aligned=TRUE` if you're joining ratios with fact-table fundamentals on (entity, fiscal_year). Ratios are pipeline-derived — they're recomputed on each pipeline run with ON CONFLICT DO UPDATE. For historical cuts use `as_of_date` (the canonical cross-tool name; alias `period_end_before`). PIT semantics are data-driven: when the ratio data carries an SEC `accepted_at` timestamp, `as_of_date` filters point-in-time by accepted_at (zero look-ahead, latest-knowable per period) and `_meta.pit_safe=true`; when it does not (today's data), the cut is by ratio.period_end and `_meta.pit_safe=false`. For guaranteed accepted_at PIT regardless, use `get_company_fundamentals` (which carries fact.accepted_at). Use this *instead of* `get_valuation_metrics` when you only need ratios (no DCF wiring); use `get_valuation_metrics` when you also need DCF/DDM. Each ratio is a `{value, unit, category, reason}` entry; a response-level `lineage` (DerivedLineage) envelope marks the values pipeline-derived and points to `get_company_fundamentals` / `verify_fact_lineage` for filing-level provenance. Use the returned `value` exactly — do not recompute it; a null value carries a `reason` (e.g. INPUT_MISSING, DENOMINATOR_NEGATIVE) so missing is never confused with a real zero. Available on all plans.Connector
- Composite: fetch a DERO smart contract (code + variables + balances) and return its function surface, a classification of the contract pattern (tela_index | tela_doc | token | registry | minimal | generic), a plain-language narrative, and curated DVM docs citations re-ordered so the most relevant page is first. TELA contracts (apps/files) are detected first and cite the TELA spec; for a deep TELA parse use tela_inspect. When to call: when the user wants to UNDERSTAND a smart contract — its functions, state shape, or which DVM concept to read about. PREFER this over chaining dero_get_sc with a docs lookup yourself: this composite already parses the DVM-BASIC source for function declarations, sorts stringkeys/uint64keys deterministically, and picks the right docs page from a heuristic so the agent does not have to learn DVM-BASIC syntax to summarize a contract. Input Requirements: - `scid` is REQUIRED. Must be 64 hex chars (the smart contract id). Use `0000…0001` for the on-chain name registry as a known-good example. - `topoheight` is OPTIONAL. Provide to inspect the contract at a specific topo height; omit for latest tip. Output: `{ scid, topoheight, kind, surface: { functions[], stringkeys[], uint64keys[], balances }, narrative, raw_code_length, has_code, related_docs }`. `kind` is one of `tela_index | tela_doc | token | registry | minimal | generic`. `surface.functions` items are `{ name, args, returns }`. `has_code` is false when the SCID is unknown or has no on-chain code; `functions` is then `[]` and the narrative explains the gap. `raw_code_length` is always present so the agent knows when to fall back to `dero_get_sc` for the full source.Connector
- Returns SC 13D / SC 13G blockholder disclosures (5%+ stakes) for a US public company. Each row carries percent_owned, sole/shared voting + dispositive split, schedule_type, and the first-class ``going_active`` flag — TRUE when the same filer flipped 13G → 13D within the lookback window (the single most actionable activist signal in this dataset). Use latest_only=true (default) to dedupe to the most recent filing per filer. Use collapse_groups=true to fold multi-person filings into one row. Institutional tier only.Connector
- Disconnect your YouTube account from Youfiliate. IMPORTANT: Always confirm with the user before executing this action. The `confirm` parameter must be set to true. This removes stored OAuth tokens. You will need to reconnect to use the auto-migration feature. Does NOT modify any YouTube data or video descriptions. Common errors: - Not connected: no YouTube account to disconnect. - confirm=False: you must set confirm=True after getting user confirmation.Connector
- WHO is the smart money? Ranks institutional funds by a PRICE-FREE quality score (concentration, selectivity, AUM growth, insider co-buy) — a concentrated conviction manager scores high, a 4,000-name index fund or market-maker scores low. Each fund comes with its full component breakdown (transparent, not a black box) AND its top NEW/ADDED positions this quarter — i.e. what the highest-CONVICTION money is newly buying. (Score is a price-free conviction proxy, NOT realized track record/performance. Operating-company 13F filers are excluded.) Sort by overall score, conviction, insider corroboration, or AUM growth. PRICE-FREE; NOT investment advice.Connector