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184,844 tools. Last updated 2026-06-08 20:05

"namespace:io.github.Kubedoll-Heavy-Industries" matching MCP tools:

  • Live capability snapshot of the responder's GPU sidecar — extensions[] (e.g. gpu, clay-v1.5, prithvi-eo2), cuda_available, models_loaded[], healthy, last_polled_unix_s. Refreshed every 30 s by a background poller; reads are constant-time. When to use: Call before scheduling a GPU-heavy plan (Clay / Prithvi / Galileo embeddings, foundation-anchored algorithms) so the agent knows whether the GPU tier is up *right now* without per-request /health round-trips. Pair with `emem_topics` (its `algorithm_availability` map says which algorithm keys can run given the current capabilities) and `emem_explain_algorithm` (full inference-tier metadata per algorithm). When `extensions[]` is empty the sidecar is unreachable — only CPU/scalar/cached tiers will produce facts; foundation-anchored materializers will sign Absence with `gpu_unavailable` reason.
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  • Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).
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  • Return the full citation-anchored specification for one Eurorack module by id. Use this when the user names a specific module and you want its specs (HP, power, jacks, parameters), capabilities (envelope, quantizer, logic, etc.), or firmware history. The typed prose fields (jack/parameter/mode descriptions) are paraphrased summaries; manual_outline → get_manual_chunk give the verbatim manual prose to quote against. How much to quote and overall answer shape live in SKILL.md (the "Answer shape" section + §8 citation) — this description is the data contract. ## Provenance fields Every typed row in the response — capabilities[], jacks[], parameters[], modes[], firmware_versions[], plus nested zones/assignments/tracking — carries a source_id pointing at the source the claim was extracted from. Cross-reference list_references(module_id=...) for the source title and canonical_url. The typed prose fields — jacks[].description, parameters[].behavior, modes[].description, capabilities[].notes, firmware_versions[].notes — are extractor-synthesized summaries grounded in the manual, NOT verbatim quotes. Treat them as the corpus's stated claim about the field; they're authoritative for what the field *does*, but they are not direct manual text. For verbatim quotation in your answer, always pull the actual prose via get_manual_chunk(chunk_id) — the description fields are the typed claim, not the source quote. manual_outline[] bundles a lightweight outline of the module's manual prose — one entry per chunk with heading, source, and a ~140-char preview snippet. Always scan it before answering — for prose-shaped questions to find the relevant chunk, for spec-shaped questions to find a chunk to quote alongside the typed data. When a snippet looks relevant, call get_manual_chunk(chunk_id) to pull the full text. manual_outline_total is set ONLY when the outline was truncated for a verbose module; its absence means the returned outline is complete. When set, use search_manual to reach chunks beyond the cap. Module IDs are slug-shaped: "<manufacturer-id>/<module-slug>". For example: - alm-busy-circuits/pamelas-new-workout - make-noise/maths ## Optional args — trim the payload, target the outline By default this returns the full spec. For narrow questions you can shrink it: - view: "concise" returns just the id-card fields (name, manufacturer, hp, description, capabilities, production_status, replaced_by) and drops the heavy arrays — use it for triage ("which of these is the quantizer?") or when you only need to confirm what a module is. "full" (default) returns everything. Ignored when fields is set. - fields: array of top-level keys to include (e.g. ["jacks","parameters"]). id and _meta are always returned. Use this for a quick jacks-only or specs-only read instead of paying for character[]/common_problems[]/role_fitness[]/the full manual_outline. Takes precedence over view. - heading_filter: case-insensitive substring on manual_outline heading_path — e.g. "calibration" returns only outline chunks under a Calibration heading, so you skip scanning a long outline. - outline_offset / outline_limit: page through manual_outline (default 100 per page, hard max 250). Combined with manual_outline_total this lets you reach chunks past the cap without falling back to search_manual. Returns: - id, name, manufacturer { id, name } - hp, depth_mm - power: { plus_12, minus_12, plus_5 } (mA) - description (manufacturer's prose summary, citation-backed) - capabilities[]: functional tags with per-module realization notes (source_id per row) - jacks[]: inputs and outputs with voltage range, signal_type, prose description (a paraphrased summary, NOT a verbatim quote — to quote the manual, pull the source prose via get_manual_chunk), plus assignments[] for assignable jacks (destination menu — empty for fixed-function jacks). When mirrors_parameter is set, the jack mirrors that knob's current assignment (e.g. Pizza CTRL CV mirrors the CTRL knob). normalled_from { id, name } is set when this input has a hardware normal — i.e. when unpatched, it receives the signal at the named source jack (e.g. Multigrain GATE normalled from NEXT). null when no normal exists. V/Oct inputs may carry an optional tracking { tracking_range_octaves, tracking_quality, temperature_compensated } object — present only on jacks that have been audited for V/Oct metadata. Fields inside may be null when the source is silent on that aspect. Optional _field_absent: { <field_name>: { source_id, note } } records fields that were audited and found to be source-silent — read it before hedging: an entry under voltage_min means "the manual doesn't state this" (so a confident "the manual doesn't specify" answer is appropriate); the field being null *without* an entry means "not yet extracted" (hedge differently — recommend the user check the manual). - parameters[]: knobs, switches, menu settings with range, unit, behavior (paraphrased summary, NOT a verbatim quote — same as jacks[].description; quote get_manual_chunk for source text), plus zones[] (labeled regions along the control's travel — e.g. Swells FLOW "Sine" / "Random" halves, optionally mode-scoped) and assignments[] (destination menu for assignable knobs/menu-settings) — both empty arrays for plain controls. Modal-module params may also carry per_mode_notes (rebinding text keyed by mode_id slug, present only when the param rebinds per mode — e.g. Plaits MORPH, Swells EBB/FLOW). Same _field_absent convention as jacks[] — when default_value is null and _field_absent.default_value is present, the manual doesn't state a default. - modes[]: mode list for modal modules (Plaits, Swells, MFX) — { id, label, description, behavior_model_id, scope? }. Empty for modeless modules. Mode ids cross-reference parameters[].per_mode_notes keys and parameters[].zones[].mode_id. Optional scope is set when modes are selectable independently per member rather than module-wide — 'per-segment' (Stages hold/ramp/step), 'per-envelope' (Tangrams cycle/single), 'per-output' (PNW), 'per-channel'. Member count is carried by the corresponding enumerated parameters/jacks (e.g. Stages' six Type Button N parameters), not duplicated on the mode rows. - hidden_functions[]: functions reached via a trigger other than a single labeled control — { id, trigger_type, affected_control, label, description }. trigger_type is a controlled vocabulary ('long-press' | 'hold' | 'combo' | 'double-press' | 'power-on-hold' | 'held-turn') so recall/menu-diving load is countable; affected_control names the panel control the trigger acts on (null for module-global functions like hold-on-power-up calibration). Empty for modules whose controls are all directly labeled. Read this for "how do I get to X?" / menu-diving questions and when assessing how much hidden state a module carries — the same info used to live buried in parameters[].behavior prose. - panel_sections[]: manufacturer-named regions of the front panel (e.g. Multigrain "Dedicated Sound CV inputs" grouping GATE/NEXT/SELECT, "Morph section" grouping the MORPH knob + MORPH CV jack). Each entry has { label, description, members: [{ kind, id, name }] } where members cross-reference jacks[] / parameters[] by id. Empty for modules without manufacturer-named groupings. - character[]: curated subjective-character claims (vocal/aggressive/clean/gritty/lush/...) with source citations. Read this when the user asks about *sound* or *feel* rather than specs — filter choice for "carve rhythmic transients" or "warm pad voice" hinges on character, which the typed-fields surface can't carry. Each entry: { tag, note (prose elaboration), source_id (when archived in sources), citation_url + citation_quote (when sourced from a review/forum/video we don't archive per-module) }. Empty for modules that haven't been character-audited yet — distinguish "empty array, audit pending" from "no character worth noting." Tags are open-vocab; common starter set: vocal, aggressive, clean, gritty, acidic, lush, dark, bright, smooth, woody, formant, screaming. - common_problems[]: curated first-aid lore — repeatable failure modes that owners hit but the manual doesn't cover (calibration drift, hum, screen offset, firmware-flash brick recovery, bus-normalling caveats). Read this when the user asks "anything I should watch out for with X?" or describes a symptom matching a known module quirk. Each entry: { problem_summary (one sentence), cause (prose), fix_or_workaround (prose), confidence ('confirmed' | 'likely' | 'anecdotal'), source_id, citation_url, citation_quote }. Empty array means "no curated problems on file" — agents should NOT extrapolate to "no known problems"; the audit is opt-in per module and most modules have not been touched yet. - role_fitness[]: role-realization rollup — canonical techniques whose role_realizations this module fills, with the affordances it brings to that role. Use this when the user wants to know "what roles can this module play?" — e.g. Optomix → lpg role in low-pass-gate-pluck, affordances_provided=[lowpass-gate]. Each entry: { technique_id, technique_label, role_id, role_label, affordances_provided, notes }. Pair with list_techniques(filter={ module_id }) for the full role_definition + sibling realizations, or find_role_realizations(technique_id, role_id) to substitute other modules into the same role. - firmware_versions[]: version + release_date (may be partial: YYYY | YYYY-MM | YYYY-MM-DD) + notes (per-version changelog prose when the source provides one — e.g. "Added Smooth Random waveform type. Added Logic parameter (AND/OR/XOR)."). Use this to answer "what changed in v2?" without web search. - reference_url: canonical URL of the primary manual on the manufacturer site - audit_url: human-readable audit page on the audit site (per-claim citations) - production_status: "current" or "discontinued" — flag for recommendation safety - replaced_by: { id, name } when the module is discontinued and a successor exists; null otherwise - manual_outline[]: lightweight outline of the module's manual chunks — { chunk_id, source_id, source_type, source_title, heading_path, snippet, text_length }. Ordered by (source_id, chunk_index). When the snippet looks worth reading in full, call get_manual_chunk(chunk_id). Empty when no manual prose has been ingested yet for this module. - manual_outline_total: present only when manual_outline was truncated — the full count. Hit search_manual to reach the rest. - _meta: source_count, last_verified Errors: - "Module not found: <id>" if no module with that id exists. If the user asks something the manual does not cover (e.g. subjective "is this good for percussion?"), say so explicitly — never confabulate from spec data.
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  • Composite: in one call, recommend the best LLC structure for a user's situation. Combines audience matching (against the 22 audiences served by `list_audiences` / `get_audience`) with a deterministic rule engine over the dimension fields. Returns a concrete `recommended_structure` slug (`wyoming_llc_single` | `foundation_stack` | `operator_shield` | `wyoming_llc_starter` | `consultation_recommended`), `rationale[]`, `recommended_addons[]`, an `estimated_total`, a `confidence` band, a brand-voice-clean `narrative`, the matched audience slug when found, a `next_tool_suggestion` for chaining, citations, and `_diagnostics` exposing what signals fired. When to call: when the user describes their situation (profession, jurisdiction lean, investor count, IP needs, budget) and wants a single structured recommendation — before `start_anonymous_llc` (which begins the action) or `design_entity_bundle` (which assumes a multi-entity choice has already been made). PREFER `run_privacy_architecture_assessment` when the user wants to be guided through a longer question-by-question flow. Call `request_consultation` only when this tool's response carries `confidence: "consultation_recommended"` AND the user agrees. Input Requirements: - All fields OPTIONAL but at least ONE of `scenario_text`, `audience_type`, `jurisdiction_preference`, `ip_holding`, `investor_count`, or `budget_tier` MUST be provided. An empty call returns a structured `INVALID_INPUT` error. - `scenario_text` is free-text from the user (e.g. "Texas content creator, no investors, IP-heavy, $5k budget"). The tool extracts budget, investor count, and IP signal via regex when present. - `audience_type` is OPTIONAL but PREFER passing a known audience slug from `list_audiences` when the user's profession matches (e.g. `doctors`, `accountants`, `high-net-worth`). Input is normalized to kebab-case. - `jurisdiction_preference` is one of `Wyoming | New Mexico | Delaware`. `ip_holding` is boolean. `investor_count` is a non-negative integer. `budget_tier` is one of `starter | standard | premium`. Output: `{ recommended_structure, rationale, recommended_addons, estimated_total, confidence, narrative, audience_match, next_tool_suggestion, related_docs, _diagnostics }`. `confidence: "high"` when an audience matched or rule engine had concrete signals; `"default_baseline"` when input was thin-but-parseable (returns the warm Wyoming starter); `"consultation_recommended"` when the situation needs custom design (investors, multi-entity, cross-border). PREFER citing `/protect` for next-step action and the matched `/for/<slug>` audience page when one was returned in `audience_match`. Quote the `narrative` verbatim — it's brand-voice-clean. Do NOT quote `estimated_total` as a guarantee — it's a planning estimate. Never cite `/pricing` from this tool; the recommendation flow guides the user toward action, not the price page directly.
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  • Subscribe to industry (NACE) monitoring: when any Norwegian company in the chosen industry triggers a monitored event (new announcement, status change such as bankruptcy/dissolution, or ownership update), Firmaradar delivers a webhook to your URL. Use list_nace_codes first to resolve the exact code. A subscription on a parent code matches all child codes. Restrict `events` (e.g. ['status_changed']) and use geographic/size filters to cut volume in large industries, or pick a digest `aggregation_mode`. Idempotent — upserted on (user, nace_code), so re-subscribing the same code updates it. Requires a user whose plan has Firmaovervakning enabled. Call only when the user has asked to set up industry monitoring.
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  • Indian capital market intelligence for the IN diaspora (30M+) and investors. Covers NSE, BSE, and MCA corporate registry across four modes: • company — full company profile: name, CIN, exchange, NSE/BSE tickers, industry, incorporation date, paid-up capital, registered office, status, directors • market_quote — real-time quote: price (INR), change%, volume, market cap, P/E ratio. Sources: Yahoo Finance (primary), BSE API, NSE API (proxy-gated) • sector_overview — Nifty/Sensex sector snapshot: top 5 companies by market cap. Supported sectors: it, banking, pharma, energy, auto, fmcg, realestate, metals, telecom, consumer • mca_filing — Ministry of Corporate Affairs filings. Requires CIN for direct lookup. Input formats accepted: • NSE ticker (e.g. 'RELIANCE', 'TCS.NS') • BSE 6-digit code (e.g. '500325' for Reliance) • CIN 21-char (e.g. 'L17110MH1973PLC019786') • Company name EN (e.g. 'Reliance Industries', 'Tata Consultancy') • Sector keyword (e.g. 'IT services', 'banking', 'pharma') ENV: AICI_RESEARCH_PROXY_URL with country-in routing unlocks NSE direct API and MCA.
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  • Give your AI assistant access to real Helm chart data. No more hallucinated values.yaml files.

  • Screens public GitHub repos and PRs to generate risk maps, findings, and merge-readiness signals.

  • Execute point-in-time queries for one or more engineering metrics. Returns current metric values for specified time periods, with support for batch queries and optional period-over-period comparisons. Time range (startTime/endTime) cannot exceed 6 months (180 days). PREREQUISITES - Follow this workflow: 1. Discover all available metrics ONCE: Call listMetricDefinitions (view='basic') - cache this response 2. Get metric query metadata ONCE per metric: Call listMetricDefinitions (view='full', key=METRIC_KEY) - supportedAggregations: Valid aggregation methods - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Cache the full view response for each metric. Reuse the metadata from cached responses for subsequent queries on the same metric. 3. Construct query: Use the query metadata from the full view responses in step 2 to build valid point-in-time requests IMPORTANT: Cache only results from listMetricDefinitions. Do NOT cache point-in-time query results - always execute fresh queries for current data. Only refresh cached listMetricDefinitions responses if no longer in your context window or explicitly requested. Do NOT guess attribute names - always use exact values from listMetricDefinitions responses. Response includes: - Lightweight metadata: Column definitions optimized for programmatic use - Row data: Actual metric values and dimensional data - No heavy schemas: Source definitions excluded (get from listMetricDefinitions instead) Error responses: - 400: Invalid metric names, date range, validation errors, or unsupported metric combinations - 403: Feature not enabled (contact help@cortex.io)
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  • Batched get_module — returns `{ modules: [...], errors: [...] }` with full citation-anchored specs for up to 25 modules in one call. Prefer this over multiple get_module calls when you have a known list of modules to fetch (e.g. preparing to call draw_patch_diagram across N modules, comparing several candidates side-by-side). One round trip vs. N. Args: - module_ids: array of "<manufacturer-id>/<module-slug>" strings. Up to 25 per call; duplicates are deduplicated. Optional args (apply to every module in the batch, same semantics as get_module): - view: "concise" returns the id-card subset (name, manufacturer, hp, description, capabilities, production_status, replaced_by) for every module and drops the heavy arrays — the cheapest way to triage a list ("which of these are LFOs?"). "full" (default) returns complete specs. Ignored when fields is set. - fields: top-level keys to include on each module (e.g. ["jacks","parameters"]). id and _meta are always returned. Use this when you only need a slice across N modules (e.g. just jacks for draw_patch_diagram) instead of N full specs. - heading_filter / outline_offset / outline_limit: narrow and paginate each module's manual_outline. Returns: - modules[]: GetModuleResponse for each id that resolved (same shape as get_module; narrowed when fields is set). - errors[]: { id, message } for each id that failed (e.g. unknown module). Other ids in the batch still resolve. If you only need a single module, use get_module — same shape, one element. Need only jacks (e.g. for draw_patch_diagram)? Pass fields: ["jacks"] to skip the full specs.
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  • Give any agent eyes. Pass any public URL → get back a structured intelligence report: page title, meta tags, all headings (H1–H6), full body text, every form mapped with fields and input types, all links, images, and pattern detection (prices, emails, dates). Anomaly flags included: JS-heavy SPA, Cloudflare challenge, CAPTCHA, access restrictions. One tool call turns a blind agent into one that can observe anything on the internet. No Playwright config. No browser infra to spin up. x711 is the browser — agent never touches it. Returns: { title, meta, headings, body_text, links, forms, images, detected: {prices, emails, dates}, anomalies, note }. Cost: $0.03. Pair with x711_agent_act to complete the full browser loop.
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  • Take answers from the Privacy Architecture Assessment (the multi-step questionnaire that maps user situation → recommended LLC structure) and return a structure recommendation with rationale. When to call: when the user is uncertain what business structure they need (single LLC, holding + operating, Series LLC) and is willing to answer questions, OR when an agent has already gathered the relevant answers (intent, jurisdiction preference, IP-heavy / employee-heavy / investor-count, budget). PREFER `design_entity_bundle` after this tool returns a multi-entity recommendation. Input Requirements: - `answers` is REQUIRED. A record keyed by question ID with string answer values. Pass at minimum the user's primary `intent` (e.g. `real-estate`, `crypto`, `creator`, `high-income`); structurally richer answers produce more specific recommendations. Output: `{ recommended_structure, rationale, recommended_jurisdiction, recommended_addons, narrative, related_docs }`. `narrative` is the agent-quotable plain-language explanation. PREFER citing the structure-decision guide. If the user accepts the recommendation, follow up with `start_anonymous_llc` (single entity) or `design_entity_bundle` (multi-entity).
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  • Free discovery: corpus-backed SELLER reputation for a merchant/provider wallet. Answers "is this provider organic, narrow, or a wash fleet?" from the merchant's inbound payment graph over the last 90 days: unique payers, repeat-payer %, heavy-fleet revenue concentration, captive-payer % (onboarding-sink proxy), and a scripted-fleet uniformity signal. Returns a wash_label + reputation tier + wash_flagged bool. Complements score_wallet_for_intel (payer side) with the seller side. Fail-open: a DB gap returns wash_label/tier "unknown" rather than erroring. This is the free seller signal; the paid per-wallet renorm model + signed V5 receipt remain at GET /v1/intel/trust/{wallet} (0.05 USDC).
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  • Paid tier only. Calling this without an authenticated CivilQuants account returns TIER_INSUFFICIENT — sign up at https://civilquants.com/pricing or use the free-tier alternative compute_end_area_earthworks. Highway / surface-water drainage channel installation per BS EN 1433 + SHW Cl. 511-514 + HD 33/16. Discriminates between five UK channel product classifications via the channel_type enum (linear_polymer_concrete — ACO-pattern; linear_concrete — Charcon-pattern precast; slot_drain — narrow-slot recessed; combined_kerb_drainage — Beany/Safeticurb integrated unit per SHW Cl. 514; steel_grated_channel — heavy-duty stainless/galvanised). Plus parallel discriminators on two further WorkCategory entries: outfall_type on CHANNEL_OUTFALL (end / branch / side), silt_box_type on CHANNEL_SILT_BOX (standard / heavy-duty / deep-sump). The MMHW Series 500 _highway_drainage_channel handler is the platform's FOURTH 2D-banded handler — code `500.8.{t}.{d}` bands by channel_type (5 bands) AND depth_class (4 bands). 5×4 = 20-cell grid. SMM7 routes to R12 (Drainage below ground) — SECOND highway L1 family outside Section Q after S39 lighting → V41. Example params: channel_depth_mm=100 mm (50–1500), channel_length_m=50 m (1–500), outfall_count=1 nr (0–20). Example call: {"params": {"channel_depth_mm": 100, "channel_length_m": 50, "outfall_count": 1}, "standard": "MMHW"}. Omitted parameters use sensible engineering defaults. Pass deliverables=["xlsx","dxf","pdf"] (any subset) to also receive one-shot download URLs in the same call: Excel BoQ (both tiers, watermarked free) plus the dimensioned DXF (CAD) and PDF drawing sheets (paid tier).
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  • Screen a person or organization name against the Sugra sanctions corpus. Returns a SCREENING SIGNAL, not a compliance determination. Sugra is a technology provider, not a sanctions authority or consumer reporting agency. PEP and adverse-media coverage is supplementary and non-comprehensive - a `clear` result is not proof of absence, and a `hit` is a candidate match to review, not a finding. Output is COMPACT to protect the agent context budget: `{status, matches:[{name, score, list, type}], disclaimer}`. The verdict `status` is one of `clear`, `review`, or `hit`. The heavy raw fields (match rationale, source ids, publish dates) are dropped; use the Sugra API directly when the full screening envelope is needed. Args: name: The person or organization name to screen (required). country: Optional ISO 3166-1 alpha-2 country to narrow the match. dob: Optional date of birth (YYYY-MM-DD) for a person. nationality: Optional nationality to narrow the match.
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  • General-purpose web grounding via parallel.ai (Vercel AI Gateway). Returns synthesized text excerpts plus structured sources[] with direct URLs. Use for: topic landscapes, entity-deep teardowns, recency-sharp queries, named-vendor lookups, general fact retrieval. NOT for: Reddit/X/community discourse → use search_community. NOT for: numerical effect sizes or methodology-heavy fact-check → use search_research. The agent decomposes the brief into sub-questions BEFORE calling — one focused query per call. Optional after_date (ISO YYYY-MM-DD) for fast-decay topics. Optional max_results 1-20, default 10.
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  • Calculate a Seattle-area cost estimate. Returns total, material, labor costs, days, and itemized line items. Material IDs: use get_material_options to get exact IDs, or pass a close match (e.g. "lvp", "composite", "cedar") and the server will resolve it. If ambiguous, the error message lists valid options. Required fields by projectType: interior-painting: rooms (Array<{id,name,length(ft),width(ft),height(6-30),paintCeiling(bool),paintTrim(bool),doorCount,windowCount,surfaceCondition("new-drywall"|"good-condition"|"poor-condition"),trimComplexity("baseboards-only"|"simple-trim"|"complex-trim"),wallTexture("smooth"|"light-texture"|"heavy-texture"),roomEmpty(bool)}>) | paintQuality (material ID) | paintFinish ("flat"|"eggshell"|"satin"|"semi-gloss"|"gloss") | includesPrimer (bool) | majorColorChange (bool) exterior-painting: wallArea(sqft) | trimArea(sqft) | doorCount | paintQuality (material ID) | surfacePrepLevel ("minimal"|"moderate"|"extensive") | includesPrimer (bool) | stories (1-3) | colorChange (bool) flooring: rooms (Array<{id,name,length(ft),width(ft)}>) | flooringMaterial (material ID, e.g. "vinyl-plank-lvp","solid-hardwood","ceramic-tile") | includesUnderlayment (bool) | underlaymentType? (material ID) | includesBaseboard (bool) | baseboardType? (material ID) | baseboardLinearFeet? | includesRemoval (bool) | removalType? ("carpet"|"tile"|"hardwood") | includesSubfloorPrep (bool) | transitionCount deck: deckType ("new"|"existing") | dimensions ({length,width,height(ft above ground)}) | deckingMaterial (material ID, e.g. "pressure-treated-decking","composite-decking-basic") | framingMaterial (material ID, e.g. "pressure-treated-framing-2x6") | includesRailing (bool) | railingMaterial? (material ID) | railingLinearFeet? | includesStairs (bool) | stairSteps? (0-20) | deckShape ("rectangle"|"l-shape"|"angled-corners"|"multi-level") | skirtingType ("none"|"lattice"|"matching-board") windows: windows (Array<{id,windowType(e.g."double-hung","casement","slider","bay"),width(inches 12-120),height(inches 12-120),quantity}>) | qualityLevel ("standard"|"premium"|"luxury") | includesTrimWork (bool) | trimMaterial? (material ID) | includesRemoval (bool) | energyEfficient (bool) siding: wallArea(sqft) | sidingMaterial (material ID) | includesInsulation (bool) | insulationType? (string) | homeHeight ("single-story"|"two-story"|"three-story") | includesRemoval (bool) | existingSidingType? (string) | trimLinearFeet (number) | soffit (bool) | soffitLinearFeet? (number) fence: linearFeet | fenceMaterial (material ID) | height (ft, 3-8) | gateCount (number 0-10) | gateWidth? (ft) | style? (e.g. "privacy","picket") | includesRemoval (bool) | terrain ("flat"|"sloped"|"mixed") | concreteFootings (bool) landscaping: yardArea(sqft, 100–50000) | includesIrrigation(bool) | irrigationType?(string) | irrigationZones?(1–20) | includesSod(bool) | sodSquareFeet?(defaults to yardArea) | includesMulch(bool) | mulchSquareFeet?(defaults to yardArea) | includesSitePrep(bool) | plants?(Array<{id,plantType,quantity,size("small"|"medium"|"large")}>) | hardscapeFeatures?(Array<{id,featureType,squareFeet,material}>) kitchen: kitchenSize(sqft, 40-600) | scope("cosmetic"|"standard"|"full-gut") | cabinets("keep"|"reface"|"prefab"|"semi-custom"|"custom") | countertop("keep"|"laminate"|"butcher-block"|"quartz"|"granite"|"marble") | appliances("keep"|"budget"|"mid-range"|"premium") | flooring("keep"|"lvp"|"tile"|"hardwood") | backsplash?(bool) | plumbingRelocation?(bool) | electricalUpgrade?(bool) | island?(bool) | lighting?(bool) bathroom: bathroomType("half-bath"|"full-bath"|"primary-bath"|"accessible") | bathroomSize(sqft, 20-300) | scope("cosmetic"|"standard"|"full-gut") | showerTub("keep"|"tub-to-shower"|"walk-in-shower"|"tub-replacement"|"freestanding-tub") | vanity("keep"|"budget"|"mid-range"|"premium") | tileWork("none"|"floor-only"|"floor-and-shower"|"full-tile") | heatedFloors?(bool) | newLighting?(bool) | ventilation?(bool) | plumbingRelocation?(bool) ada: projectScope("single-room"|"whole-home"|"bathroom-specific") | modifications({grabBars?,walkInShower?,widerDoorways?,ramp?,nonSlipFlooring?,leverHandles?,raisedToilet?,rollUnderSink?,accessibleCounters?,stairLift?} all bool) | currentCondition("minor"|"moderate"|"major") | homeStories?(1-3) | doorwayCount?(0-20) | rampLengthFeet?(0-60) | flooringSquareFeet?(0-5000)
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  • Global situational awareness. Returns the full 32x32 grid and reservoir stats. Warning: This is a heavy payload (1024 pixels). Use for broad scanning of opportunities.
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  • Full structured JSON state of a board: texts (id, x, y, content, color, width, postit, author), strokes (id, points, color, author), images (id, x, y, width, height, dataUrl, thumbDataUrl, author; heavy base64 >8 kB elided to dataUrl:null, tiny images inlined). Use this for EXACT ids/coordinates/content (needed for `move`, `erase`, editing a text by id). For visual layout (where is empty space? what overlaps?) call `get_preview` instead — it's much cheaper for spatial reasoning than a huge JSON dump.
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  • List every audience-specific privacy guide Default Privacy publishes — currently 22 (doctors, accountants, realtors, content creators, high-net-worth individuals, OnlyFans creators, etc.). Each entry returns a slug, audience label, one-line headline, intent ("business" | "asset" | "emergency"), and the recommended LLC structure shape ("single" | "bundle") + state. Call `get_audience` next for the full FAQ + risks + structure rationale on a chosen slug. When to call: when the user describes their profession or situation ("I'm a doctor", "real estate agent", "OnlyFans creator", "I have a lot of assets") and you want to find a matching audience-specific guide. Also call when the user asks "what kinds of clients do you serve" or "who uses this". PREFER `get_audience` directly when the user has already named a specific audience slug. Input Requirements: none. Output: `{ audiences: [{ slug, audience, headline, intent, structureType, state }], total, citation }`. The list is sorted by slug. `structureType` is "single" for one-LLC recommendations and "bundle" for multi-entity stacks (typically high-net-worth or heavy asset-protection scenarios). PREFER quoting the matching audience's `headline` to the user and then chaining `get_audience(slug)` to retrieve the full guidance before recommending a structure.
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  • Great Britain grid carbon intensity right now (gCO2/kWh + index) + the live fuel mix + the cleanest upcoming half-hour from the 24h forecast. Use to time energy-heavy work / answer "is the grid clean now?". Keyless, CC-BY. GB only.
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  • Indian capital market intelligence for the IN diaspora (30M+) and investors. Covers NSE, BSE, and MCA corporate registry across four modes: • company — full company profile: name, CIN, exchange, NSE/BSE tickers, industry, incorporation date, paid-up capital, registered office, status, directors • market_quote — real-time quote: price (INR), change%, volume, market cap, P/E ratio. Sources: Yahoo Finance (primary), BSE API, NSE API (proxy-gated) • sector_overview — Nifty/Sensex sector snapshot: top 5 companies by market cap. Supported sectors: it, banking, pharma, energy, auto, fmcg, realestate, metals, telecom, consumer • mca_filing — Ministry of Corporate Affairs filings. Requires CIN for direct lookup. Input formats accepted: • NSE ticker (e.g. 'RELIANCE', 'TCS.NS') • BSE 6-digit code (e.g. '500325' for Reliance) • CIN 21-char (e.g. 'L17110MH1973PLC019786') • Company name EN (e.g. 'Reliance Industries', 'Tata Consultancy') • Sector keyword (e.g. 'IT services', 'banking', 'pharma') ENV: AICI_RESEARCH_PROXY_URL with country-in routing unlocks NSE direct API and MCA.
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