"NVIDIA Isaac Sim Robotics Simulation Platform" matching MCP tools:

• dispatch_social_listening_researcher_async

  Heista

  Dispatch to the SOCIAL LISTENING RESEARCHER — multi-platform community-signal interpretation. Use for: "what are practitioners saying about X across platforms / what jargon is emerging in field Y / what is the cross-platform discourse around brand/topic Z". Treats T3 community sources as primary data, distinguishes cross-platform patterns from single-platform noise. ≥3 platforms sampled per brief. Returns: Signal map (Signal / Platforms / Volume / Sentiment + recency) + Per-platform evidence trail + Cross-platform vs single-platform classification + Confidence flag + Sources. NOT for: single-source thematic work (use dispatch_qualitative_researcher) / numerical sentiment effect sizes (use dispatch_quantitative_researcher). ASYNC version: returns { job_id } immediately, the specialist runs durably on a Vercel Workflow (no 300s timeout). Use this version when the specialist is expected to take >90s. Call get_dispatch_result(job_id) periodically (respect wait_ms_hint in the response) until status === 'completed' or 'failed'. Idempotent: same brief + same org reuses the same job_id, so retries don't fan out duplicate runs.

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• senado_ecidadania_consultas_votos

  Senado BR MCP

  Acervo **histórico** de votos das consultas públicas do e-Cidadania, com **quebra por UF** (fonte: CSV Arquimedes; ~15 mil matérias, atualizado semanalmente). Diferente de `senado_ecidadania_listar_consultas` (consultas em tramitação): aqui o conjunto é o **arquivo** de matérias já consultadas — `status` vem como `Descontinuado` no arquivo de origem, por isso é tratado como acervo, não como opinião atual. Retorna `{ count, referencePeriod, consultas }`, cada item com `id`, `materia`, `ementa`, `autoria`, `votosSim`/`votosNao`/`totalVotos`, `votosPorUf` (`{ UF: { sim, nao } }`) e `url`. Use `ordenarPor` (`total`/`sim`/`nao`, padrão `total`) e `ordem` para ranking; `uf` para recortar e **ranquear por aquele estado** (só matérias com votos na UF, e cada item ganha `recorteUf`); `materia` para filtrar por código (numérico) ou trecho do nome/ementa; `limite` (padrão 20).

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• search

  AlphaAI

  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.

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• retry_run

  Hydrata - ANUGA Flood Simulation

  Retry a failed simulation run. Resets an errored run back to 'created' status and triggers a new package build. The same run ID is reused. Only valid when status is 'error'. Returns 409 for any other state.

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• sti_get_platforms

  Stimulsoft Documentation MCP Server

  List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.

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• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

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• get_investment_category_signal

  Stratalize Healthcare

  Use when evaluating VC software category attractiveness or assessing portfolio category exposure before an investment decision. Returns growth signal, top brands, and citation evidence for any software category. Example: AI infrastructure category — GROWTH signal, top brands Nvidia 67% citation share, Anthropic 18%, xAI 9% — accelerating citation growth signals sustained investment thesis. Source: Stratalize citation heuristics.

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• describe_demo

  DiscreteRate

  Full per-demo write-up: history, what it teaches, what to expect from the run_* output. Use this to ground the user before triggering a sim run, or to explain WHY the demo exists when the user asks a conceptual question about it.

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• explain_discrete_rate_simulation

  DiscreteRate

  Return a textbook-tier explainer of Discrete Rate Simulation: how it differs from DES and CT, the three primitives (Constraint / Buffer / Interrupt), paradigm integration via F2I / I2F. Use this for 'what is DRS?' / 'how is this different from DES?' / 'where does DRS fit in the simulation landscape?' style questions. Deterministic text — no engine call, no RNG.

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• list_facility_types

  QSimHealth

  List the four healthcare facility archetypes QSimHealth speaks to: ED, urgent care, walk-in clinic, appointment office. Returns one-line descriptions. Call describe_facility for detail on one type, or simulate_ed_demo to run a generic simulation.

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• show_version

  Banco Sofisa MCP

  Show the current MCP platform and adapter versions.

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• hyperscaler_deals

  DC Hub — Data Center & Power Intelligence

  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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• compare_analytical_vs_simulated

  QueueSim

  Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.

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• get_engine_versions

  olympus-bets-analytics

  Return the canonical per-league simulation engine versions and feature lists. Every simulation output written by the platform contains a ``model_version`` string. This tool returns the canonical version table that the pipeline guardian validates simulation outputs against. Args: league: Optional league filter (e.g. "NBA"). Omit to return all leagues. Returns: ``{count, engines: [{league, engine, version, key_features, ...}]}``

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• get_hbm_qualification

  Silicon Analysts

  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".

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• prepare_tx

  🐴 Gulltoppr

  Prepare a contract WRITE for the user to sign. Returns an UNSIGNED transaction, its simulation, a human-readable summary, an abi.ninja signing deeplink, and warnings. IMPORTANT: this tool NEVER signs or broadcasts. Present the summary + simulation + warnings to the user and hand them the `deeplink` — they sign in their own wallet. If `warnings` is non-empty (decompiled ABI, proxy, or a reverting simulation), surface it.

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• start_simulation

  Hydrata - ANUGA Flood Simulation

  Start a flood simulation run for a built scenario. The scenario must be in 'built' status. Returns 202 with the new run. The run transitions through: built → queued → computing → processing → complete. After starting, poll get_run_status to track progress. Returns 409 if the scenario is not in the correct state.

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• get_run_status

  Hydrata - ANUGA Flood Simulation

  Lightweight status check for a simulation run (fast, <50ms). Use this for polling instead of get_run. Returns only: id, status, progress_pct (0-100), eta_seconds, error_message, and compute_backend. Poll every 5-10 seconds. Terminal states: complete, error, cancelled.

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• get_run

  Hydrata - ANUGA Flood Simulation

  Get full details of a simulation run including timing and results. Returns the complete run record: status, progress, timing (start/end timestamps, duration), compute details (backend, instance type, cost), mesh info, error messages, and result log. Use get_run_status for lightweight polling; use this for final results.

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