"namespace:com.quantum-expectations" matching MCP tools:

• fit_historic_series

  Quantum Expectations

  Fit a log-linear trend (ln(value) = slope * year + intercept) to one historic series — fidelity or qubit-count — for one hardware type. Atomic primitive: compose with list_current_quantum_computers, compute_required_error_rate, or your own modelling to answer "when might hardware reach X?". residualStdDev is the BIASED (maximum-likelihood) RMS — divides by n, not (n - 2); on small series (n ≈ 3–5) inflate by √(n / (n - 2)) before building confidence intervals.

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• get_seller

  Kifly — Agentic Commerce & Payments

  Retrieve a seller's public profile: name, location (city/region/country), storefront URL, delivery fee, delivery coverage, and catalog size. **Call this before `create_cart` or `set_shipping_address` to validate that the seller ships to the buyer's area or to set expectations about catalog size.** `delivery_coverage` is `{ states, cities, nationwide, state_count, city_count, coverage_configured }` — the US state codes (e.g. 'CA', 'NE') and city names the seller delivers to; an address is eligible when its region matches a covered state OR its city matches a covered city. This is the full profile — `cities` is never capped here (unlike `list_sellers`/`search_products`). **`coverage_configured: false` means the seller has set up NO delivery yet — they ship NOWHERE; never tell the buyer they ship anywhere (an empty `states` list is NOT nationwide). `nationwide: true` means all 50 states, with `states` omitted to save space.** `delivery_fee_cents` is the flat fee added at checkout; `catalog_size` is the total number of products listed. **For network (cross-seller) tokens, pass `handle` to name which seller you're asking about** (e.g. `handle: 'bay-clothing-district'`). Handle lookup is case-insensitive — 'BayClothingDistrict' and 'bayclothingdistrict' both resolve. Seller-scoped tokens may omit `handle` — their own seller is implicit.

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• get_nist_ai_rmf_requirements

  Stratalize Governance

  Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

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• check_llc_name_availability

  Default Privacy

  Provide DIY entity-name verification links for Wyoming / New Mexico / Delaware. **This tool does NOT perform a live Secretary-of-State availability check** — the partner API has no such endpoint and we do not scrape state registries. Agents must not quote this tool's response as if it were a live registry lookup. When to call: when the user wants to verify a name before submitting it, OR before `start_anonymous_llc` to set expectations. Pair with `suggest_llc_entity_names` to generate alternatives if the user is unsure. The output points the user at the official state search UI; they perform the check themselves. Input Requirements: - `names` is REQUIRED. An array of entity-name bases (without the LLC suffix). - `jurisdiction` is OPTIONAL. One of `Wyoming | New Mexico | Delaware`. Drives which state's SOS search URL is included. Output: `{ jurisdiction, names_checked, availability: "unverified", manual_search_url, instructions, related_docs }`. The `availability` value is literally the string `"unverified"` — there is no `available: true/false` field, by design. PREFER citing the DIY-check guide and the state SOS search URL verbatim. Tell the user the state validates availability at filing time; if a name is rejected, our team works with them on an alternate. Do not promise automatic refund on rejection.

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• get_seller

  Kifly — Agentic Commerce & Payments

  Retrieve a seller's public profile: name, location (city/region/country), storefront URL, delivery fee, delivery coverage, and catalog size. **Call this before `create_cart` or `set_shipping_address` to validate that the seller ships to the buyer's area or to set expectations about catalog size.** `delivery_coverage` is `{ states, cities, nationwide, state_count, city_count, coverage_configured }` — the US state codes (e.g. 'CA', 'NE') and city names the seller delivers to; an address is eligible when its region matches a covered state OR its city matches a covered city. This is the full profile — `cities` is never capped here (unlike `list_sellers`/`search_products`). **`coverage_configured: false` means the seller has set up NO delivery yet — they ship NOWHERE; never tell the buyer they ship anywhere (an empty `states` list is NOT nationwide). `nationwide: true` means all 50 states, with `states` omitted to save space.** `delivery_fee_cents` is the flat fee added at checkout; `catalog_size` is the total number of products listed. **For network (cross-seller) tokens, pass `handle` to name which seller you're asking about** (e.g. `handle: 'bay-clothing-district'`). Handle lookup is case-insensitive — 'BayClothingDistrict' and 'bayclothingdistrict' both resolve. Seller-scoped tokens may omit `handle` — their own seller is implicit.

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• list_hardware_timings

  Quantum Expectations

  Return per-platform gate-cycle timings (2Q gate time, readout time, in SI seconds) plus the representative device and native 2Q gate name, with source URLs. Joins list_current_quantum_computers via `hardwareType`. Use for runtime estimates, ratio analysis, or as inputs to compute_quantum_volume_rate. Values are representative current-generation numbers, not records.

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• compute_quantum_volume_rate

  Quantum Expectations

  Compute the Quantum Volume Rate (QV/second): QVR = V_Q / (log2(V_Q) * t_2Q + t_meas). First-order estimate of how fast a device prepares one QV-sized square circuit (one native 2Q gate per QV layer + one end-of-circuit measurement). OVERSTATES achievable rate: real compilation inflates the 2Q-gate count per layer; omits reset/SPAM, mid-circuit measurement, and classical-control latency. For a production throughput metric, see IBM's CLOPS (arXiv:2110.14108).

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• get_neighbor_permit_activity

  Kolmo Construction

  Aggregate permit activity within ~1500 ft of a Seattle-area parcel over the last 24 months. Returns total count, breakdown by category, and recent example permits (anonymized — no addresses). Sourced from city open-data portals (Socrata). Currently supports Seattle; other jurisdictions return jurisdictionSupported=false. Use to gauge neighborhood activity before quoting an unusual project, or to set homeowner expectations on what neighbors have built.

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• focus_expectations

  Bcb Br

  Focus market expectations survey — annual median/mean forecasts from ~150 economists. Filter by indicator (e.g. "IPCA", "Selic", "PIB Total", "Câmbio", "IGP-M") and optionally reference year. Returns latest survey rows with Media, Mediana, Minimo, Maximo, numeroRespondentes.

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• get_earnings_intelligence

  Stocklake — AI Stock Intelligence

  Upcoming earnings with AI context — flag scores, verdicts, and risk factors per stock. Combines the earnings calendar with AI pipeline data to surface which upcoming earnings events are worth monitoring. Parameters: - days_ahead: look-ahead window in days (default 14, max 30) - sector: filter to one sector (e.g. "Technology") - min_flag_score: only return stocks with AI flag score >= this value (optional) Returns per stock (sorted by earnings_date ascending): - earnings_date: ISO UTC timestamp · is_estimate: whether date is estimated - symbol, name, sector, price, rsi, market_cap - eps_trailing, eps_forward (earnings expectations context) - ai_verdict, ai_flag_score, ai_confidence (nightly AI pipeline) - ai_risks: top 2 AI-identified risk factors - analyst_rating, analyst_target Pro tier only — AI pipeline cost attached. For informational purposes only. Not financial advice.

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• list_example_algorithms

  Quantum Expectations

  Return the curated list of example quantum algorithms with published resource estimates (qubit count, depth/gate count, source paper URL). Useful for comparing what algorithms need vs. what hardware can deliver.

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• list_qldpc_codes

  Quantum Expectations

  Return the catalog of supported qLDPC codes (id, label, family, n, k, d, circuitLevelDistance, ancilla counts, roundsPerLogicalOp, threshold, prefactor [per block per syndrome cycle], logicalErrorExponent [= d_circ/2], source URLs). Use a code's `id` as the `errorCorrectionCode` input to `compute_expectation`.

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• GetEarningsHistory

  Fintel Discovery — Financial Intelligence for AI Agents

  Fetch earnings history (EPS actual vs estimate, surprise %) and upcoming earnings dates with consensus estimates. Also returns forward EPS estimates by quarter and fiscal year. Use this tool when: - You want to see how a company has performed vs EPS expectations - You need the next earnings date and the consensus estimate - You are analyzing earnings surprise trends or growth trajectory Returns three sections: earnings_history, earnings_dates, earnings_estimate. Source: Yahoo Finance via yfinance. No API key required.

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• burn-rate-calculator

  Microapp

  Compute a startup's gross burn, net burn, and runway from starting cash, ending cash, the period in months, and optional monthly revenue. Returns a health label benchmarked to standard SaaS funding-stage runway expectations.

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• firecrawl_feedback

  firecrawl-mcp

  Send structured feedback for a completed Firecrawl v2 job. Use this for endpoint-level feedback on `scrape`, `parse`, `map`, or `search` jobs when the job result was useful, partially useful, or failed to meet expectations. For search-result quality specifically, prefer `firecrawl_search_feedback` when available because it has search-focused guidance. This generic tool posts to `/v2/feedback` and accepts endpoint-wide signals: - **endpoint** — one of `search`, `scrape`, `parse`, or `map`. - **jobId** — the id returned by that endpoint. - **rating** — overall result quality: `good`, `partial`, or `bad`. - **issues** — stable lowercase issue codes such as `missing_markdown`, `bad_pdf_parse`, or `wrong_links`. - **tags** — optional lowercase tags for grouping feedback. - **note** — short human-readable context. Do not include huge page contents or raw scrape results. - **url**, **pageNumbers**, and **metadata** — small contextual fields that identify what the feedback refers to. Do not store multi-MB outputs in feedback. Use concise notes, issue codes, URLs, and page numbers. **Returns:** `{ success, feedbackId, creditsRefunded, creditsRefundedToday?, dailyRefundCap?, dailyCapReached?, alreadySubmitted?, warning? }` JSON.

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• get_analyst_estimates

  Signal8

  Get forward analyst estimates for a company including EPS, revenue, EBITDA, and net income (low/high/avg) with analyst counts. Supports annual and quarterly periods. Use when analyzing forward earnings expectations or revenue forecasts.

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• get_neighbor_permit_activity

  kolmo-mcp-server

  Aggregate permit activity within ~1500 ft of a Seattle-area parcel over the last 24 months. Returns total count, breakdown by category, and recent example permits (anonymized — no addresses). Sourced from city open-data portals (Socrata). Currently supports Seattle; other jurisdictions return jurisdictionSupported=false. Use to gauge neighborhood activity before quoting an unusual project, or to set homeowner expectations on what neighbors have built.

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• get_model_risk_management_standards

  Stratalize Governance

  Use when preparing for a model risk management examination, building an SR 26-2 compliant model governance program, or assessing a financial institution's MRM framework against regulatory expectations. Returns Federal Reserve SR 26-2 and OCC requirements across development, independent validation, ongoing monitoring, and governance — with exam deficiency rates showing where institutions most commonly fail. For AI and ML models, SR 26-2 explicitly requires independent validation even for vendor-supplied models and black-box systems. Example: Documentation deficiencies are the most common exam finding at 67% of reviewed institutions — inadequate conceptual soundness documentation for credit scoring models triggers immediate MRA (Matter Requiring Attention). Source: Federal Reserve SR 26-2, OCC Bulletin 2026-13, FDIC FIL-15-2026.

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• get_earnings

  Signal8

  Get historical earnings data for a company including EPS actual vs estimate, revenue actual vs estimate, and surprise percentages. Use when analyzing earnings beats/misses or upcoming earnings expectations.

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• compute_expectation

  Quantum Expectations

  Given a quantum circuit (2-qubit error rate p, qubit count n, depth d), compute the effective error rate, success probability, and optional surface-code or qLDPC overhead. The response is self-describing (formulas, assumptions, caveats, glossary, SOTA hardware, historic series with source URLs) so an agent can reason from one call. For the inverse ("what hardware do I need?") use compute_required_error_rate; to rank multiple platforms in one call use compare_hardware_scenarios.

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