"NVIDIA" matching MCP tools:

• tako_searchA

  TakoA

  Search Tako's data graph and the live web in one call: many results at once, as structured cards plus web results, with the top card rendered inline as a chart. It finds data; `tako_contents` fetches it. Each card carries a headline value, node ids, and a url — pass the url to `tako_contents` for rows (`exportable: true` cards) or a web result's full page text. When `exportable` is false the rows are locked — read the headline value from the card's `description`. Best for: breadth — fan out several narrow queries in parallel. Each query resolves one metric — for one entity, or a comparison set ("Apple revenue", "Nvidia vs AMD gross margin"); several metrics or topics in one query retrieve poorly. To learn what Tako covers, or a metric's canonical name, run `tako_available_data` first, then search on the canonical name it returns.

  ConnectorNo auth

• query_intersectA

  NVIDIA NemoClaw CKGA

  Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.

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• query_intersectA

  NVIDIA AI CKGA

  Answer a conjunctive query: concepts reachable from EVERY anchor at once (A AND B). query_ckg walks outward from one concept. This intersects the reachable sets of two or more, which is the shape of most real questions — "the component that satisfies A AND applies to B". Neither anchor alone answers it; the answer lives in the overlap. Every branch is an exact set of declared edges, so the intersection is exact. A concept appears only if a declared path reaches it from each anchor. A relation missing from the graph produces an empty result, never a guess. Args: branches: Two or more branches. Either a bare anchor ("TensorRT-LLM"), which takes everything within `depth` hops, or an anchor plus an explicit relation path using '>' ("TensorRT-LLM > REQUIRES > ENABLES"), where each relation replaces the frontier. '*' matches any relation. Mix both forms freely. domain: Domain name from list_domains(). depth: Hops for bare-anchor branches, 1-5 (default 2). Ignored for explicit paths. direction: 'out' follows dependencies, 'in' follows them backwards, 'both' (default). mode: 'AND' (default) intersects branches; 'OR' unions them. limit: Max concepts listed, 1-200 (default 40). The true count is always shown. Returns: Markdown with the query plan and its per-step set sizes, then the answer set with taxonomy tags. Reports which branch was empty when the intersection is empty.

  ConnectorNo auth

• get_hbm_qualificationA

  Silicon AnalystsA

  Sourced HBM qualification tracker: which memory vendor (SK Hynix, Samsung, Micron) passed which AI-accelerator customer's qualification (NVIDIA Vera Rubin/GB300/B300/H200, AMD MI350/MI325X, Broadcom), by generation (HBM3/HBM3E/HBM4) and stack height. Returns `matrix` (current status per vendor×customer×generation, each row dated + source URL + confidence) and `timelines` (per-relationship status-change history back to 2022, e.g. sampling → in_qualification → qualified → volume_shipping). Refreshed daily; status changes human-reviewed. USE THIS for: "who supplies HBM4 for Vera Rubin?", "did Samsung pass NVIDIA qualification?", "Micron HBM4 status", qualification timeline/history questions, HBM supply-eligibility analysis. DO NOT USE for: HBM pricing/market share (use get_hbm_market_data); per-chip HBM cost (use get_accelerator_costs). Filters: vendor (enum), customer (substring), generation (enum), include_timelines (boolean). Anonymous callers may receive timelines truncated to the latest event per relationship — full history with a free API key (https://siliconanalysts.com/developers). Cite as "Silicon Analysts — HBM Qualification Tracker".

  ConnectorNo auth

• rerank_evaluateA

  IA-QA — 130+ QA & Dev Tools for AI AgentsA

  Evaluate RAG retrieval quality: rank passages against a query and compute Precision@k / Recall@k plus a PASS/FAIL CI verdict from ground-truth relevance labels. Three modes, all keyless except the last. (1) BYO scores — give each passage the `score` your own reranker produced (Cohere, Jina, a self-hosted NIM, a cross-encoder): deterministic, offline, and it evaluates YOUR reranker rather than someone else's. This is the mode to gate CI on. (2) Default, no scores and no key — ranks with local BM25, a lexical keyword baseline: it answers "does a keyword floor already surface my relevant passages?", never "is my neural reranker good". (3) Live NVIDIA reranker — supply `api_key` for an NVIDIA account that still has reranking entitlement; NVIDIA retired its hosted reranking endpoints on 2026-05-18, so this one is for accounts that were grandfathered in.

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• get_kg_factsA

  NightWatch Live IntelligenceA

  NightWatch Knowledge Graph lookup for a COMPANY/entity (equities & RWA universe: Samsung 'samsung-electronics', SK Hynix 'sk-hynix', 'tsmc', 'nvidia', 'asml', 'arm', ...). Returns SOURCED data only — every row carries a citation URL (the KG refuses uncited data): (1) numeric fundamentals (revenue, net income, market cap, business segments, dividend, market-share rankings), (2) typed relations (supplies / competes / customer_of / licenses — e.g. Samsung supplies NVIDIA HBM, competes with TSMC in foundry), and (3) a live HyperLiquid price block when the entity is tradable. Use this BEFORE reasoning about a company's fundamentals, competitors, supply chain, or a hedge on its equity perp. Input accepts a slug or a plain company name (fuzzy-matched).

  ConnectorNo auth

• get_edgar_insider_tradesA

  FinBridgeA

  Latest insider transactions for a US company, parsed from SEC Form 4 filings, with a buy-vs-sell summary and an optional buy/sell filter. Each trade lists the reporting insider, their relationship, and non-derivative (common stock) transactions. Insider BUYS (open-market purchases, code P) are a stronger sentiment signal than sells (code S), which happen for many reasons (diversification, taxes). Use tx_type to monitor one side. Args: - company (required): ticker / company name / CIK - limit: number of most-recent Form 4 filings to parse, 1-25 (default 10) - tx_type: 'all' (default) | 'buy' (code P purchases only) | 'sell' (code S sales only) Returns: {company:{cik, name, ticker}, tx_type, summary:{buys:{count,shares,value}, sells:{count,shares,value}}, count, trades:[{filedAt, owner, relationship, url, transactions:[{date, code, shares, price_per_share, acquired_or_disposed, shares_owned_after}]}], notes}. summary totals cover the whole fetched window regardless of the filter; value = shares x price where a price is reported. Transaction codes: P=open-market purchase, S=open-market sale, M=option exercise, F=shares withheld for tax, A=award/grant, G=gift. acquired_or_disposed: A=acquired, D=disposed. Examples: - "insider BUYING at Apple" -> {company:'AAPL', tx_type:'buy'} - "recent insider SELLING at Nvidia" -> {company:'NVDA', tx_type:'sell'} - "all TSLA insider activity, more history" -> {company:'TSLA', limit:25} Use when: monitoring insider buy/sell activity (officers, directors, 10% owners) for a US-listed company. Larger 'limit' widens the time window. Don't use for: institutional holdings (use get_edgar_13f), Korean companies, or derivative-only detail (option grids are skipped). Errors: unknown company -> use search_edgar_company; a filter with no matching transactions returns count 0 (not an error); unparseable Form 4 XMLs are skipped and counted in notes.

  ConnectorOAuth

• searchA

  mcpA

  Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.

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• get_edgar_insider_tradesA

  FinBridgeA

  Latest insider transactions for a US company, parsed from SEC Form 4 filings, with a buy-vs-sell summary and an optional buy/sell filter. Each trade lists the reporting insider, their relationship, and non-derivative (common stock) transactions. Insider BUYS (open-market purchases, code P) are a stronger sentiment signal than sells (code S), which happen for many reasons (diversification, taxes). Use tx_type to monitor one side. Args: - company (required): ticker / company name / CIK - limit: number of most-recent Form 4 filings to parse, 1-25 (default 10) - tx_type: 'all' (default) | 'buy' (code P purchases only) | 'sell' (code S sales only) Returns: {company:{cik, name, ticker}, tx_type, summary:{buys:{count,shares,value}, sells:{count,shares,value}}, count, trades:[{filedAt, owner, relationship, url, transactions:[{date, code, shares, price_per_share, acquired_or_disposed, shares_owned_after}]}], notes}. summary totals cover the whole fetched window regardless of the filter; value = shares x price where a price is reported. Transaction codes: P=open-market purchase, S=open-market sale, M=option exercise, F=shares withheld for tax, A=award/grant, G=gift. acquired_or_disposed: A=acquired, D=disposed. Examples: - "insider BUYING at Apple" -> {company:'AAPL', tx_type:'buy'} - "recent insider SELLING at Nvidia" -> {company:'NVDA', tx_type:'sell'} - "all TSLA insider activity, more history" -> {company:'TSLA', limit:25} Use when: monitoring insider buy/sell activity (officers, directors, 10% owners) for a US-listed company. Larger 'limit' widens the time window. Don't use for: institutional holdings (use get_edgar_13f), Korean companies, or derivative-only detail (option grids are skipped). Errors: unknown company -> use search_edgar_company; a filter with no matching transactions returns count 0 (not an error); unparseable Form 4 XMLs are skipped and counted in notes.

  ConnectorOAuth

• route_queryA

  NVIDIA AI CKGA

  Route an NVIDIA AI question to the optimal model and reasoning approach via graph depth. The CKG graph IS the router — hop depth is a deterministic complexity metric. Deeper NVIDIA prerequisite chains (CUDA → TensorRT → TensorRT-LLM → NIM) require more capable models. No heuristic: the graph decides. Routing table: hop_depth 1 → haiku · direct (simple lookup) hop_depth 2 → sonnet · generic_cot (moderate chain) hop_depth 3+ → opus · sparql_cot (deep dependency, structured reasoning) Args: question: Concept name or natural language question about NVIDIA AI. domain: Domain from list_domains() — e.g. "nvidia-tensorrt-triton", "nvidia-nim". Returns: model_tier + reasoning_approach + why + context subgraph to inject before LLM call.

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• hyperscaler_dealsA

  DC Hub — Data Center Site Selection & Colocation: Electricity, Power Grid, Gas, FiberA

  Hyperscaler AI Deal Tracker — live feed of Stargate, OpenAI, Anthropic, Microsoft, Oracle, CoreWeave, AMD, NVIDIA, sovereign-AI deals. Pulls from dchub news pipeline, extracts $-figures + MW via regex, classifies by actor. 10-min refresh. Use for tracking AI capex events ($1B+/week typical), capacity announcements, and competitive intel. Do NOT use for the full historical M&A comp set (use list_transactions) or a single-deal teardown with grid context (use deal_autopsy); this is the live $1B+ AI-capex feed.

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• scrutica_get_sovereign_programA

  ScruticaA

  Fetch detailed data on a national sovereign AI compute program. Returns: announced_usd, announced_govt_only_usd, committed_usd, disbursed_usd, reality_ratio, status, key_partners, governance_reach, NVIDIA/US dependency, source_count. 'list_all' returns a summary table of all tracked programs. READ THE TWO ANNOUNCED FIELDS BEFORE COMPARING COUNTRIES. announced_usd is the all-capital headline and rolls private commitments in with government money, so it is NOT comparable across countries as-is: the UAE line ($518.74B) and France ($201.34B) are dominated by private vehicles (Stargate UAE, SoftBank), while the US line ($2.6B) is essentially NAIRR alone. announced_govt_only_usd is the government-only figure and is the comparable one. reality_ratio = disbursed_usd / announced_govt_only_usd (falling back to announced_usd only when no government-only figure exists). It is deliberately NOT disbursed_usd / announced_usd. Each row includes reality_ratio_denominator_usd and reality_ratio_denominator_field so the division is reproducible from the payload. reality_ratio is null for most programs because disbursed_usd is unpublished — see reality_ratio_unavailable_reason. Absence concentrates in the least transparent programs (Saudi, China, Korea, UAE all report no disbursed figure), so a ranking by reality_ratio is a ranking of DISCLOSURE, not of execution; do not present it as the latter.

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• diagnose_sglangA

  self-hosted-aiA

  Validate an SGLang configuration for NVIDIA DGX Spark (GB10/SM121A). Pure pattern-matching against known failure modes documented in the Sovereign AI Blog. No inference, no external calls. Returns critical issues, non-fatal warnings, and a recommended baseline config. All parameters are optional; supply only what you have. With no inputs you get the recommended config and a 'unknown' verdict.

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• find_paths_betweenA

  Wafergraph MCPA

  Every documented supply path between two companies, following supplier->customer edges (e.g. 'how does NVIDIA actually depend on Shin-Etsu'). Searches up to max_depth hops in one or both directions and returns each path as an ordered list of companies, shortest first. Capped for combinatorial safety; absence of a path means undocumented, not disproven — see edge_coverage.

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• get_prerequisitesA

  NVIDIA AI CKGA

  Return the full ordered prerequisite chain for a concept — everything to learn first. Args: concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'. domain: Domain name from list_domains().

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• verify_sourceA

  NVIDIA AI CKGA

  Return the source URL and SHA-256 content hash for any NVIDIA AI concept node. Audit chain: edge answer → graph commit → source_content_hash → source_url (fetch hint). Verification: curl -s <source_url> | sha256sum # compare to source_hash Args: concept: Concept label (partial match supported). domain: Domain from list_domains() — e.g. 'nvidia-nim', 'nvidia-tensorrt-triton'.

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• myrsu_analyze_riskA

  myrsu-mcpA

  Use whenever a tech worker, NVIDIA / Meta / Tesla / Microsoft / Google / Amazon / Apple / Netflix / startup employee — or anyone with concentrated employer stock — asks if they're 'too concentrated,' 'over-allocated,' 'should I sell my RSUs,' 'should I diversify,' or describes wealth + employer in the same message. Calculates the Single-Company Risk Score (0-100), full concentration analysis, top action items, historical drawdown context, and a pre-filled dashboard URL. All fields optional except an employer (ticker OR explicit volatility); the more inputs the better the analysis. International — pass `country` (US/IN/CA/UK/EU/AU/OTHER) to switch retirement-account terminology and currency symbol. Risk math is identical for all countries. Stateless and privacy-respecting — no inputs are logged or stored.

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• query_ckgA

  Salesforce AgentForce CKGA

  Traverse the AgentForce knowledge graph from any concept. Returns prerequisites (what this concept needs) and dependents (what it enables). Every relationship traces to an authoritative Salesforce doc URL. Args: concept: Concept name — e.g. 'Autonomous Resolution', 'Einstein Trust Layer', 'Service Agent', 'Grounding', 'NVIDIA NIM'. depth: Traversal depth 1–5 (default 3).

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• verify_sourceA

  NVIDIA NemoClaw CKGA

  Return the authoritative source URL and content hash for a NemoClaw concept node. Every node in the CKG was declared from a specific source document. This tool returns the source URL (where the node came from) and the SHA-256 hash of that document's bytes at extraction time. A hash mismatch on re-fetch means either the source has changed (stale edge — re-extract) or the graph was patched without re-fetching (silent edit — investigate). Audit chain: edge answer → graph commit → source_hash → source_url (fetch hint) Verification: curl -s <source_url> | sha256sum # compare output to source_hash Args: concept: Exact or partial concept label (e.g. 'CorporateCA', 'L7Proxy').

  ConnectorNo auth

• searchA

  AlphaAIA

  ChatGPT connector contract: search AlphaAI's AI-enriched financial news with a natural-language query. Ticker symbols (NVDA, BTC-USD), company names (nvidia, tesla) and topic words (insider, earnings, ipo, crypto…) in the query are resolved to structured filters; a query that names nothing known returns the freshest high-relevance market stories. Each result carries an id for the fetch tool. For precise filtered queries prefer alphai_news_search.

  ConnectorAPI key

• diagnose_sglangA

  Sovereign AI BlogA

  Validate an SGLang configuration for NVIDIA DGX Spark (GB10/SM121A). Pure pattern-matching against known failure modes documented in the Sovereign AI Blog. No inference, no external calls. Returns critical issues, non-fatal warnings, and a recommended baseline config. All parameters are optional; supply only what you have. With no inputs you get the recommended config and a 'unknown' verdict.

  ConnectorNo auth