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"author:Shanko-26" matching MCP tools:

  • Looks up static metadata for one of twenty-seven nakshatras by exact name and returns interpretive, professional, activity, and body-map reference data. SECTION: WHAT THIS TOOL COVERS Vedanga/classical reference only — no chart computation. Covers deity, ruler, symbol, gana, nature, classical vs modern prose, profession vectors, life themes, keywords, strengths/challenges, favourable vs unfavourable activities, and body_map. Names are case-sensitive exact matches (Ashwini … Revati list). It does not compute birth nakshatra from BirthData (use asterwise_get_natal_chart). SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: None. SECTION: INPUT CONTRACT nakshatra_name is forwarded raw — no local fuzzy matching or normalisation. SECTION: OUTPUT CONTRACT data.name (string) data.index (int — 0–26) data.interpretation: source (string) nakshatra_number (int) name (string) sanskrit (string) span (string) symbol (string) deity (string) ruling_planet (string) sign (string) sign_lord (string) gana (string) nature (string) body_part (string) classical_qualities[] (string array) appearance — { classical (string), modern (string) } nature_description — { classical (string), modern (string) } profession — { primary[] (string array), secondary[] (string array), note (string), modern (string) } life_themes — { core, karmic_path, challenge, gift, modern (strings) } keywords[] (string array) strengths[] (string array) challenges[] (string array) data.activities: favorable_activities[] (string array) unfavorable_activities[] (string array) data.body_map: parts[] (string array) sensitivity (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — name passes straight through. INVALID_PARAMS (upstream): — None — unknown names surface as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Exact spelling required — no fuzzy recovery. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — computes birth nakshatra from time/place, not encyclopaedic copy. asterwise_get_dasha — uses Moon nakshatra for timing, not this lookup table.
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  • "What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since `since`), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). `since` accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.
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  • Returns the current status of a task created by an `?async=true` intel request. Poll this endpoint until `status` is one of: `complete`, `failed`, `cancelled`, `expired`. On `complete`, the `result` field contains the same payload the sync endpoint would have returned. On `failed`, `error.message` explains the failure. Use this tool when: - You submitted an intel probe with `?async=true` and need to retrieve the result. - You want to check whether a background task finished without opening an SSE stream. Do NOT use this tool when: - You want real-time event streaming — use `stream_task` instead. - You have no task_id — submit a probe with `?async=true` first. Inputs: - `task_id` (path, required): 26-char ULID returned in the 202 response. Returns: - `status`: `pending` | `running` | `complete` | `failed` | `cancelled` | `expired`. - `result`: populated when status is `complete`. Null otherwise. - `error`: populated when status is `failed`. Null otherwise. - `expires_at`: tasks expire 1 hour after creation. Cost: - Free. Does not count against rate limits. Latency: - Typical: <100ms.
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  • Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.
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  • Lists all Brazilian states from IBGE. Features: - Lists all 27 states (26 states + Federal District) - Filter by region (North, Northeast, Southeast, South, Central-West) - Sort by ID, name, or abbreviation Examples: - List all states: (no parameters) - Northeast states: regiao="NE" - Sorted by abbreviation: ordenar="sigla" Use a different tool when: - Municipalities of a state → ibge_municipios - Details/hierarchy of one locality by code → ibge_localidade Behavior: read-only and idempotent — a live GET against the public IBGE Localidades API. Returns a Markdown table.
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  • Forces the bank to re-sync one or more connections NOW and WAITS for it to finish (PATCH /items/:id, then polls until the item stops updating, up to ~60s). Use this when a balance or transaction list looks stale: a connection can read UPDATED yet be hours old, and this pulls fresh data WITHOUT disconnecting/reconnecting. Pass `items` as an array of selectors (item_id, connector_id, or connector_name); OMIT `items` to sync ALL linked banks. Returns `{ results, errors }`; each result has the final `status`, `executionStatus`, `lastUpdatedAt` (advances when data is refreshed), and `synced` (true = fresh data is ready). `needs_action` (e.g. MFA_REINTERACTION / LOGIN_ERROR / WAITING_USER_INPUT) means the user must re-authenticate — those results include a `reconnect_url` that opens the widget in UPDATE mode for that exact connection (user enters credentials / MFA token, data refreshes in place, no slot consumed, no disconnect needed). `timed_out: true` means the sync is still running — re-check with openfinance_get_item_status. Set `wait: false` for fire-and-forget (returns immediately while UPDATING).
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Matching MCP Connectors

  • Computes Panchanga elements for one calendar date at a geographic location and returns tithi, vara, nakshatra, yoga, karana, and end times in UTC. SECTION: WHAT THIS TOOL COVERS Derives classical Panchanga limbs from sidereal astronomy for the given date, latitude, longitude, and timezone — no birth time or natal chart. data.yoga here is the Panchanga Yoga (Sun+Moon nakshatra composite), wholly separate from natal yogas in asterwise_get_yogas. It does not score muhurta windows across ranges (asterwise_get_muhurta), list Choghadiya slices (asterwise_get_choghadiya), or monthly calendars (asterwise_get_panchanga_calendar). SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_choghadiya — same-day slot quality for the location. SECTION: INPUT CONTRACT date must be YYYY-MM-DD (Pydantic pattern on LocationInput). lat/lon bounds are validated locally. Upstream rejects calendar dates outside 1900–2100. timezone defaults to Asia/Kolkata when the caller leaves the default in LocationInput. SECTION: OUTPUT CONTRACT data.tithi: number (int — 1–30) name (string) paksha (string — 'Shukla' or 'Krishna') degrees_elapsed (float) degrees_remaining (float) end_time (string — ISO UTC) data.vara: number (int — 1=Ravivar through 7=Shanivar) name (string) lord (string) end_time (string) data.nakshatra: index (int — 0–26) name (string) pada (int — 1–4) degrees_elapsed (float) degrees_remaining (float) end_time (string — ISO UTC) data.yoga: index (int — 0–26) name (string) is_inauspicious (bool) degrees_elapsed (float) end_time (string — ISO UTC) data.karana: number (int) name (string) degrees_elapsed (float) degrees_remaining (float) end_time (string — ISO UTC) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — date not YYYY-MM-DD or invalid calendar day → MCP INVALID_PARAMS — lat/lon outside allowed ranges → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — None — dates outside 1900–2100 surface as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Panchanga yoga is unrelated to asterwise_get_yogas natal combinations. SECTION: DO NOT CONFUSE WITH asterwise_get_yogas — natal chart yogas, not Panchanga Sun–Moon yoga. asterwise_get_panchanga_calendar — whole-month daily rows, not a single day.
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  • "Tell me about X" / "research Acme" / "brief me on Tesla" / "what does Apple do" / "company profile for Microsoft" / "give me the rundown on NVDA" / "everything you know about $TICKER" — full cross-source profile of a US public company in ONE parallel call. ALWAYS PREFER over chaining single-pack SEC/XBRL/news lookups when the user asks for a holistic view. Fans out across SEC EDGAR, XBRL, USPTO, news, GLEIF and returns: cik + company_name; recent_filings (up to 5 with pipeworx://edgar/company/{cik}/filings/{accession} URIs); fundamentals (LATEST 10-K Revenues + NetIncomeLoss + Cash, sorted period_end DESC); patents (USPTO PatentsView API sunset May 2025 — soft-fails until reactivated); recent news mentions via GDELT→GNews fallback; LEI via GLEIF. Pass ticker "AAPL" or zero-padded CIK "0000320193" — names not supported (use resolve_entity first if you only have a name).
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  • Opens a persistent SSE connection that emits events as the task progresses. The stream closes automatically when the task reaches a terminal state or after ~90 seconds (timeout). Heartbeat comments are sent every ~15 seconds to keep the connection alive through proxies. Event types: - `status` — emitted when status changes (pending → running → complete/failed) - `result` — emitted on `complete` with the full result payload - `error` — emitted on `failed`, `cancelled`, or `expired` with error info - SSE comment (`: heartbeat`) — keepalive, no data Use this tool when: - You want real-time progress without polling. - You are in an environment that supports SSE (EventSource API). Do NOT use this tool when: - You want a simple one-shot status check — use `get_task` instead. - Your HTTP client doesn't support streaming responses. Inputs: - `task_id` (path, required): 26-char ULID. Returns: - SSE stream (`text/event-stream`). Each event is `event: <type>\\ndata: <json>\\n\\n`. Cost: - Free. Counts as one request against rate limits when the stream opens. Latency: - First event: <200ms. Stream duration: up to 90s.
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  • Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.
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  • ACCOUNT REQUIRED (free — sign in via GitHub at https://pipeworx.io/signup; depth:"thorough" needs a paid plan). If you are not signed in, use ask_pipeworx instead — it works on every tier. Grounded multi-source research across Pipeworx's 1337 STRUCTURED data sources (SEC filings, FRED/BLS economics, FDA, USPTO patents, markets, science, government records, etc.) in ONE call — this is NOT open-web search. Decomposes your question into focused facets, routes each to the right one of 5,073 tools IN PARALLEL, and returns a findings packet: verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation per finding, with explicit gaps[] for facets the data couldn't answer (never invented). Best for broad/multi-part questions over structured data ("compare X and Y's regulatory + financial exposure", "research the filings + market picture for ACME"). For a single lookup use ask_pipeworx (one LLM call, not many). For BREAKING or colloquial CURRENT-NEWS / "what's the world saying about X" topics, prefer ask_pipeworx — it routes to live news APIs and the *-news-feeds packs; deep_research returns mostly empty gaps[] when the topic isn't in the structured catalog. Second-hop iteration: depth:"standard" re-angles unanswered gaps (gap recovery); depth:"thorough" additionally chases the best leads from the first pass — so multi-step questions resolve in one call. Every finding carries a `hop` field and a citation_uri (record-level pipeworx:// when the source emits one, else source-level). "standard" and "thorough" also return contradictions[] flagging findings that disagree. Large records are semantically excerpted to the passages relevant to each facet (not head-truncated), so answers deep in a long filing/series aren't missed. Expect 15-60s (thorough with its follow-up + contradiction pass: up to ~90s).
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  • Generates a short-form video from a text prompt (T2V) or a text prompt + starting image (I2V). Submits, polls, and returns the final video URL. Default model is 'grok-imagine-t2v' (fast, 4-6 cr/s, with built-in KIE -> fal.ai fallback). Use list_video_models for the full lineup with credit cost per second. I2V models (e.g. 'grok-imagine-i2v', 'seedance-pro-i2v') require a public `imageUrl`. Video generation can take 30s to several minutes; this tool polls with up to an 8-minute budget. ## Model selection guide for videos (when the user does not specify a model) Default: `grok-imagine-t2v` (4-6 cr/s, fast, has KIE -> fal.ai fallback for redundancy. Best general-purpose). Pick a different model when the prompt has these signals: - "highest quality" / "premium" / broadcast / commercial -> `veo3.1-quality` or `veo3-quality` (Google's flagship, fixed 350-560 cr for 8s, 3-5 min) - "fast premium" / quick high-quality -> `veo3-fast` or `veo3.1-fast` (84 cr fixed for 8s) - Cinematic camera moves / dolly / pan -> `seedance-pro-t2v` (3-10 cr/s) or `kling-3.0-pro-t2v` (26 cr/s) - Realistic human motion / faces -> `hailuo-2.3-pro-i2v` (I2V, supply imageUrl) - Talking head / lip sync -> `kling-avatar-pro` (23 cr/s) or `infinitalk` (5-17 cr/s) - Anime / stylized / fantasy -> `wan-2.7-t2v` - NSFW / adult -> `wan-22-nsfw-i2v` (I2V only; auto-tags adult) - Animate this exact image -> any I2V variant (`grok-imagine-i2v`, `seedance-pro-i2v`, `hailuo-2.3-pro-i2v`) - First + last frame interpolation -> `seedance-pro-i2v` with both `imageUrl` + `endImageUrl` - Cheapest test -> `hailuo-2.0-standard` @ 512p (3 cr/s, ~18 cr for 6s) or `grok-imagine-t2v` @ 480p (4 cr/s, ~24 cr for 6s) - Clip 12-15s -> `grok-imagine-t2v` (accepts up to 15s) - True 4K -> `kling-3.0-4k-t2v` (94 cr/s, expensive but native 4K) **Audio in generated video:** `grok-imagine-t2v`, `seedance-pro-t2v`, and the VEO 3.x family include audio at base cost (no surcharge). Kling 2.6 and Kling 3.0 are the outliers — they price audio as a +50-100% surcharge (Kling 2.6 doubles the cost, Kling 3.0 Pro adds ~46%). Default to Grok / Seedance / VEO when sound matters and you don't want to think about audio pricing. **Cost framing:** resolution and duration drive cost more than model choice. A 6-second 480p Grok generation costs ~24 cr; the same prompt at 1080p Seedance 2 is ~858 cr (35x more). Pick the lowest acceptable resolution + duration first. **For I2V models:** `imageUrl` is required. For first+last-frame models, pass `endImageUrl` too. Ask the user only when: - Single generation would cost more than 100 credits and they haven't confirmed - They asked for "the best" with no other signal; surface 2-3 options with cost ranges
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  • Returns transactions for a bank account (BANK or CREDIT type). For CREDIT (credit card) accounts, this is the ONLY way to get itemized transactions (purchases, subscriptions, etc.). Each credit card transaction MAY carry `creditCardMetadata.billId` pointing at a bill from openfinance_list_credit_card_bills, but this is a per-connector HINT, not authoritative: some connectors (e.g. Nubank) populate it sparsely (many transactions and installments arrive with no billId) or inconsistently (the same payment tagged to more than one bill). Do NOT reconstruct a bill's total by summing transactions by billId — the bill's own `totalAmount` from openfinance_list_credit_card_bills is the source of truth. CREDIT PENDING vs POSTED varies by connector: where the bank exposes future-dated `status:'PENDING'` installments, those represent the OPEN bill plus future bills (future months); where it does NOT, only the last closed bill's POSTED items appear until ~closing. Same query, different coverage per bank (upstream). To get a standardized open-bill total / total debt regardless, use openfinance_list_credit_card_bills (`open_bill` / `total_pending_debt`). Supports from/to date filters (ISO YYYY-MM-DD) and an optional keyword filter via `search_queries` (case- and accent-insensitive substring match against description and merchant name, OR semantics across multiple terms). When `search_queries` is set the tool aggregates up to 5000 transactions within from/to before filtering — narrow from/to if `truncated:true` is returned. PAGINATION: OMIT `page` (the default) to get ALL transactions in the from/to range in one call — the tool auto-paginates the upstream and returns them under a single logical page (`page:1`, `totalPages:1`), up to a 5000 ceiling (`truncated:true` + warning if exceeded, then narrow from/to). Pass an explicit `page` (with `page_size`, max 500) only if you want to walk pages manually instead. On upstream errors, returns { total:0, results:[], warning, error } instead of throwing. `detail` controls how much per-row data you get (default `'compact'` = slim, cheap). Use `detail:'rich'` to enrich each row (when the bank connector provides it) with `merchantInfo` (estabelecimento: businessName/razão social, cnpj, cnae, category — useful for auto-classifying spending) and extra `creditCardMetadata` fields: `billId` (a per-connector HINT toward the transaction's bill — sparse/inconsistent on some connectors like Nubank, so do NOT sum by it to get a bill total; use the bill's `totalAmount` instead), `purchaseDate`, `payeeMCC`, `feeType`/`feeTypeAdditionalInfo`, `otherCreditsType`/`otherCreditsAdditionalInfo`. Use `detail:'raw'` to get the FULL untouched Pluggy transaction object (everything Pluggy returns, un-normalized — heaviest, for when you need a field we don't project). 'rich'/'raw' add tokens per row and coverage varies by bank/Open Finance, so keep the default for normal listings. For the card's statement closing/due dates use openfinance_list_accounts (`creditData.balanceCloseDate` / `balanceDueDate`). The response opens with an `account` echo block ({ account_id, bank, name, number, type, item_id }) identifying WHICH account/bank these transactions belong to. When more than one bank is connected, ALWAYS cross-check the echo against the account you intended to query and name the bank when presenting results — never attribute one bank's transactions to another. If total is 0 for a CREDIT account, check the connection health via openfinance_get_item_status — `statusDetail.creditCards.isUpdated: false` means the credit card sync failed and a force sync (openfinance_force_sync) or reconnection may be needed. May include a `provider_incident` block when the Open Finance provider has an OPEN incident affecting a connected bank: transactions may come back incomplete or wrong until the provider recovers, and reconnecting does not fix it. Bulk support: accepts account_ids for batched execution.
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  • Returns CLOSED credit card bills for a CREDIT-type account: dueDate, totalAmount, minimumPaymentAmount, allowsInstallments, plus `payments[]` (id, paymentDate, amount, valueType, paymentMode), `payments_count`, `payments_total`, finance charges aggregates, and a derived `payment_status` per bill. IMPORTANT — Brazilian Open Finance semantics: Pluggy does NOT return a `paid`/`status` field. The payment goes into the `payments[]` of the bill whose CYCLE contains the paymentDate (closing ≈ dueDate − 7d): pre-payment before close stays on the bill being paid; payment between close and due, or after due, lands on the NEXT bill. So `payments[]` on a bill commonly carries the previous bill's payment, NOT the current one's — do NOT assume this bill was paid just because `payments[]` is non-empty. Use the derived `payment_status` (`PAID` | `OPEN` | `PAST_DUE_UNCONFIRMED` | `PAST_DUE_UNPAID`): a bill is `PAID` when its OWN `payments[]` (early pre-payment) or ANY newer bill in the payload contains a payment with amount ≈ this bill's `totalAmount` (±R$0.50). The MOST RECENT bill that's past-due, with no own pre-payment match, cannot be confirmed via cross-bill (the next cycle hasn't closed yet) — it returns `PAST_DUE_UNCONFIRMED`. NEVER call such a bill 'vencida' categorically; flag that the payment may have been made between close and due and not yet reflected upstream. The full `payment_status_legend` is returned alongside the results. OPEN BILL & TOTAL DEBT (standardized, derived — OPT-IN): pass `include_open_bill:true` to ALSO get `open_bill` (the current not-yet-closed bill, próxima a vencer) and `total_pending_debt` (saldo devedor total = all pending installments), BOTH derived from PENDING transactions so they mean the same thing across connectors — use these instead of the CREDIT account's `balance`, whose meaning VARIES by connector (some report the open-bill partial, others the full installment debt). `open_bill` = { available, method (`cycle_dates` = real close/due dates | `calendar_month_fallback` = estimated, `confidence:'low'`), close_date, due_date, total_amount (net charges − credits), transaction_count }; plus a `future_bills[]` breakdown per month — LOW-confidence forward projections of PENDING installments (`confidence:'low'`, `basis`), NOT authoritative bills (for closed months trust the `results` `totalAmount`). CONNECTOR ASYMMETRY: where the bank does NOT expose the open bill before closing (only closed bills, no reliable cycle dates), `open_bill.available` is `false` with a `reason` (`connector_exposes_no_pending` or `open_bill_not_published`) — that bill isn't retrievable by any endpoint until it closes (upstream limit of the institution's Open Finance feed, not our filter); check the bank app for the current open bill. When per-transaction billId grouping does not reconcile with the bills' totals, a `bill_grouping_reliability` warning is attached (trust `totalAmount`, do not sum by billId). Default `false` (the projection runs an extra accounts+transactions scan, so it's opt-in). The response opens with an `account` echo block ({ account_id, bank, name, number, type, item_id }) identifying WHICH card/bank these bills belong to. When more than one bank is connected, ALWAYS cross-check the echo against the card you intended to query and name the bank when presenting results — never attribute one bank's bills to another. This tool's `results` are bill-level summaries — NOT individual transactions, and each bill's `totalAmount` (from the bank) is the AUTHORITATIVE amount. To see itemized purchases/charges, use openfinance_list_transactions with the CREDIT account_id — but note `creditCardMetadata.billId` is a per-connector hint that can be sparse/inconsistent (e.g. Nubank), so do NOT reconstruct a bill total by summing transactions by billId. Returns a warning instead of failing if the CREDIT_CARDS product is not enabled. Bulk support: accepts account_ids for batched execution.
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  • Renvoie toutes les versions historiques d'un article de loi, du plus ancien au plus récent. Utile pour construire une "timeline" de l'article et comprendre son évolution (ex : un article modifié en 1964, 1994, 2016 aura 3-4 lignes avec `date_debut`, `date_fin`, `etat`, `texte` distincts). Args: code: code court (voir get_law_article pour la liste des 26 codes/textes) num: numéro de l'article Returns: dict avec `code`, `code_long`, `num`, `count`, `versions` (liste ordonnée par `date_debut` ascendante).
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  • Builds a KP natal chart with sub-lords on grahas and twelve cusps from BirthData using the KP ayanamsa in the response. SECTION: WHAT THIS TOOL COVERS Krishnamurti Paddhati charting: lagna row, planet rows with nakshatra_index and sub_lord, house_cusps with matching lords. Recommend BirthData.ayanamsa='kp' for coherent physics — any enum value is accepted locally and forwarded. Accurate birth time matters; midnight placeholder yields unreliable sub-lords for event timing. Not radix (asterwise_get_natal_chart) nor prashna (asterwise_get_prashna_chart). SECTION: WORKFLOW BEFORE: RECOMMENDED — cross-check birth record before trusting sub-lords. AFTER: asterwise_get_kp_significators — house-level significator chains. SECTION: INPUT CONTRACT ayanamsa choice is not forced locally — mismatched settings still post to upstream. time='00:00' is accepted without warning. SECTION: OUTPUT CONTRACT data.ayanamsa (string — 'kp') data.lagna: rashi (string) rashi_index (int) longitude (float) nakshatra_lord (string) sub_lord (string) data.planets{} — Sun..Ketu: longitude (float) rashi_index (int) rashi (string) degree (float) is_retrograde (bool) house (int) nakshatra_index (int — 0–26) nakshatra_lord (string) sub_lord (string) data.house_cusps{} — keys '1'..'12': longitude (float) rashi_index (int) rashi (string) nakshatra_lord (string) sub_lord (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — BirthData Pydantic only. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Sub-lord chains degrade when true birth time unknown. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — classical bundle without KP sub-lords. asterwise_get_kp_ruling_planets — live moment rulers, not natal cusps.
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  • Returns a personalised daily prediction using Tarabala and Chandrabala. SECTION: WHAT THIS TOOL COVERS Computes the individual's daily auspiciousness score by: 1. TARABALA: Counts from birth nakshatra to today's transit Moon nakshatra (inclusive). The remainder mod 9 gives the Tara (1=Janma, 2=Sampat/Wealth, 3=Vipat/Danger, 4=Kshema/Prosperity, 5=Pratyak/Obstacle, 6=Sadhana/Achievement, 7=Naidhana/Destruction, 8=Mitra/Friend, 9=Ati-Mitra/Great Friend). 2. CHANDRABALA: Transit Moon's house from natal Moon (favorable in houses 1,3,6,7,10,11). 3. TRANSIT NAKSHATRA QUALITY: The type of today's Moon nakshatra (Dhruva/Chara/Ugra/Tikshna/Kshipra/Mridu/Mishra) with auspicious and inauspicious activities. Combined daily score out of 4 with label (Excellent/Good/Moderate/Challenging). SECTION: WORKFLOW BEFORE: None — birth data computes everything needed. AFTER: asterwise_get_panchanga — for full daily panchanga context. SECTION: INPUT CONTRACT birth — BirthData (date, time, lat, lon, timezone). target_date (optional): YYYY-MM-DD. Defaults to today. SECTION: OUTPUT CONTRACT data.target_date (string) data.birth_nakshatra{}: name (string), index (int 0-26) data.natal_moon_sign_index (int 0-11) data.transit_moon{}: nakshatra (string), nakshatra_index (int), rashi_index (int) data.tarabala{}: tara_number (int 1-9), count_from_birth (int), name (string), meaning (string), is_favorable (bool), interpretation (string) data.chandrabala{}: moon_house_from_natal (int 1-12), is_favorable (bool), favorable_houses[] (int array) data.daily_score{}: score (int 0-4), max_score (4), label (string) data.transit_nakshatra_quality{}: nakshatra (string), quality_type (string), english (string), auspicious_for[] (string array), inauspicious_for[] (string array) data.nakshatra_activities{}: favorable[] (string array), unfavorable[] (string array) SECTION: COMPUTE CLASS MEDIUM_COMPUTE — natal chart + ephemeris Moon position. SECTION: ERROR CONTRACT INVALID_PARAMS (local): BirthData Pydantic violations → MCP INVALID_PARAMS INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_nakshatra_details — static nakshatra reference, not personalised prediction. asterwise_get_panchanga — daily panchanga (tithi, yoga, karana), not Tarabala scoring. asterwise_get_biorhythm — Western biorhythm cycles, not classical Vedic prediction.
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  • 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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  • Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
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  • Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) `topic` — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit `kalshi_event_ticker` + `polymarket_event_slug` for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning fires in two cases — (a) matched_pairs:0 with skipped_cross_type>0 means the venues frame the topic with non-equivalent bet shapes (e.g. Kalshi range_bucket point-in-time vs Polymarket cumulative_threshold touch-anywhere — no arb exists), (b) matched_pairs:0 with skipped_cross_type:0 and both venues >5 legs means the token-overlap matcher found nothing in common — events likely semantically unrelated despite the topic keyword. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period; aligned:false means spreads are mathematically meaningless across the temporal gap. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.
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