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244,616 tools. Last updated 2026-06-28 13:40

"Wear OS" matching MCP tools:

  • "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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  • 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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  • Lista reuniões de uma comissão (pela `sigla`) num intervalo `dataInicio`/`dataFim` (YYYYMMDD); sem datas, usa os últimos 30 dias. Retorna `{ sigla, periodo, count, reunioes }`, cada reunião com `codigo`, `descricao`, `data`, `hora`, `local`, `tipo` e `situacao`. Intervalos entre anos são divididos por ano internamente. Descubra a `sigla` via `senado_listar_comissoes`; use o `codigo` retornado em `senado_reuniao_comissao` para os detalhes da pauta.
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  • Lista os membros da Mesa Diretora (presidente, vice-presidentes, secretários). O parâmetro `casa` (padrão `senado`) escolhe entre `senado` (Mesa do Senado Federal) e `congresso` (Mesa do Congresso Nacional). Retorna `{ casa, mesa, count, membros }`, cada membro com `cargo`, `codigo`, `nome`, `partido` e `uf`. Para lideranças partidárias use `senado_liderancas`.
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  • Context lookup: Parse a User-Agent header string into structured browser, OS, device type, and rendering-engine components. Use to identify client capabilities from a raw UA string, e.g. when analysing server logs or request headers; does not perform any network lookups — entirely local parsing. Runs synchronously using the ua-parser-js library with no external calls. Returns a JSON object with browser.name, browser.version, os.name, os.version, device.type, device.vendor, and engine.name fields; unknown fields are empty strings.
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  • Recommends crystals from a Vedic natal chart using house lordship rules for gem selection. This is the only API that derives crystal recommendations from a computed natal chart — not from zodiac sign or chakra preference. SECTION: WHAT THIS TOOL COVERS Classical rules applied: 1. INCLUSION RULE — A planet is recommended only if it lords at least one Trikona house (1st, 5th, or 9th). If a planet lords both a Trikona and a Dusthana (6th, 8th, or 12th), the Trikona lordship prevails and the planet is still recommended. This is the dual-lordship rule — violating it produces wrong exclusions. 2. EXCLUSION RULE — A planet that does not lord any Trikona house is contraindicated. This includes pure Dusthana lords, pure Kendra lords (4th, 7th, 10th), and pure neutral lords. 3. SCORING — Crystals are scored by which Trikona lordship their planet holds: Lagna lord (1st) Navaratna +5, Uparatna +4 — primary Life Stone Yogakaraka Navaratna +5, Uparatna +4 — lords both a non-1st Kendra AND a non-1st Trikona simultaneously 9th lord Navaratna +4, Uparatna +3 — Fortune Stone (Bhagyesh) 5th lord Navaratna +3, Uparatna +2 — Lucky Stone (Panchamesh) 4. YOGAKARAKA — A planet that lords both a non-1st Kendra (4th, 7th, or 10th) AND a non-1st Trikona (5th or 9th) is the supreme benefic for that Lagna. Example: Mars for Cancer Lagna (lords 5th and 10th). 5. DANGEROUS COMBINATIONS — Pairs of recommended crystals from enemy planet camps are flagged in warnings[]: Saturn+Sun, Saturn+Mars, Jupiter+Venus, Moon+Rahu, Moon+Ketu. 6. CLASSICAL VEDIC ONLY — Crystals with vedic_correspondence='none_classical' (Labradorite, Amazonite, Black Obsidian, etc.) are never returned. These stones have no Vedic planetary assignment in the database. SECTION: WORKFLOW BEFORE: None — this tool internally computes the natal chart. No separate natal chart call required. AFTER: asterwise_get_crystal — get full detail (hardness, origins, affirmation, full caution text) on any recommended crystal by slug. AFTER: asterwise_get_remedies — broader classical remedial programme alongside gem recommendations. SECTION: INPUT CONTRACT Standard BirthData (date, time, lat, lon, timezone, ayanamsa). Defaults to Lahiri ayanamsa. time (required): Ascendant (Lagna) is time-sensitive. Inaccurate birth time changes the Lagna → changes all house lords → changes recommendations entirely. SECTION: OUTPUT CONTRACT data.natal_context{} — chart factors used for recommendations: lagna_sign (string — Ascendant sign in English, e.g. 'Libra') lagna_lord (string — classical lord of the 1st house) fifth_sign (string — 5th house sign in English) fifth_lord (string — lord of the 5th house, Panchamesh) ninth_sign (string — 9th house sign in English) ninth_lord (string — lord of the 9th house, Bhagyesh) yogakaraka (string or null — Yogakaraka planet name if one exists for this Lagna; null if none) contraindicated_lords (string array — planets that do not lord any Trikona; their gems are contraindicated) ayanamsa (string — ayanamsa used, e.g. 'lahiri') data.total (int — number of crystals returned, up to 5) data.crystals[] — recommended crystals sorted by match_score descending: slug, name, colors[], hardness_mohs (float), chakras[], element, zodiac_signs[] vedic_planet (string — the planet this crystal corresponds to) vedic_correspondence (string — always 'navaratna' or 'uparatna'; none_classical never appears) western_planet (string or null) keywords[], healing_physical, healing_emotional, healing_spiritual, description origins[], affirmation, caution (string or null — always surface this to end users) match_score (int — house lordship score; higher indicates stronger lordship basis) match_reasons (string array — which house lordship triggered this recommendation) warnings (string array — dangerous combination warnings; may be empty) 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 — full natal chart computation + crystal scoring pass. SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): BirthData Pydantic violations → MCP INVALID_PARAMS INVALID_PARAMS (upstream): Dates before 1800 or after 2100 → MCP INTERNAL_ERROR INTERNAL_ERROR: Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — An empty crystals[] is valid when no Navaratna or Uparatna Vedic gem corresponds to the Trikona lords of this specific chart. — Blue Sapphire (Saturn) and Hessonite (Rahu) carry CRITICAL cautions in their caution field — always surface this to end users before advising wear. — Rahu and Ketu do not own signs in the classical system — they never appear as lagna_lord, fifth_lord, or ninth_lord. SECTION: DO NOT CONFUSE WITH asterwise_get_gemstone_recommendations — also a chart-based gem endpoint but uses a different engine (Atmakaraka + role-based prescription vs house lordship scoring); returns gem names not crystal database entries; does not include match_score or match_reasons. asterwise_get_crystal_recommendations — recommends crystals by zodiac sign, chakra, or intention keyword (no natal chart computation; Western metaphysical matching, not classical Jyotish). asterwise_get_crystal_by_planet — lists all crystals for a Vedic planet without house context — use this for reference, not prescription.
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Matching MCP Servers

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    Enables interaction with z/OS mainframe systems via FTP, including dataset listing/download/upload, JCL job submission and monitoring, with advanced text processing and pagination support.
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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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  • Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
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  • "What's the ticker for…" / "find the CIK for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" — resolve a user-spoken NAME to the canonical/official identifier other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (returns ticker + 10-digit CIK + company_name from SEC EDGAR + pipeworx://edgar/company/{cik} citation URI; accepts ticker, CIK, or company name as input — auto-disambiguated), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/{rxcui} citation; accepts brand or generic name). Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.
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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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  • Query known vulnerabilities for a single package version across any supported ecosystem. Returns all matching OSV advisories with severity (CVSS vectors), CVE aliases, affected version ranges, and first safe version. Use osv_list_ecosystems to validate the ecosystem string before querying — ecosystem strings are case-sensitive exact matches and an invalid value returns an error, not empty results.
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  • Create, save or publish meal plan prescriptions in WebDiet. Actions: create (new prescription for patient), save (meals/foods JSON to existing prescription), publish (release prescription to patient — makes it visible on the patient portal/app). IMPORTANT: After creating and saving foods, you MUST call publish to make the prescription visible to the patient. ═══ MÉTODO DE PRESCRIÇÃO — escolha no create ═══ WebDiet tem 3 métodos: • "Convencional" (UI: "Por alimentos", URL: metodoPlanning.php) — DEFAULT e RECOMENDADO. Alimento-por-alimento. Para cálculo de macros automático (proteínas/lipídios/carboidratos/calorias) na tabela "Alimentos prescritos" detalhada, cada alimento DEVE incluir o campo "id" com o WebDiet food-DB ID numérico. Sem id, o alimento AINDA é salvo no método Convencional e aparece no card expandido da refeição com nome + medida caseira, mas sem macros. NÃO vai para a seção qualitativa — o save retorna um warning explicando. • "Equivalentes" (UI: "Por equivalentes", URL: metodoWebdiet.php) — avançado. Prescrição por grupos de equivalentes. Requer IDs do banco. • "Qualitativo" (UI: "Qualitativa", URL: metodoQualitativo.php) — texto livre por refeição. A página do Qualitativo usa estrutura de dados diferente (refs com objetos cardapio) que este adapter ainda não gera corretamente via save. Para prescrições qualitativas, recomendado: criar via UI ou usar metodo="Convencional" sem food IDs (os alimentos aparecem como texto na refeição sem macros, efeito semelhante). ═══ prescricao_json (para o save) ═══ JSON array de refeições. Cada refeição: {nome, horario, alimentos:[...]}. Cada alimento: {nome, quantidade, medida_caseira, peso_gramas, id?}. AUTO-RESOLVE: se "id" não for enviado, o adapter procura automaticamente o melhor match no banco WebDiet (mesmo catálogo do webdiet_food_search) pelo campo "nome" e preenche o id antes de salvar — com isso os macros são calculados mesmo sem pré-chamar webdiet_food_search. O save retorna auto_resolved_foods {resolved, not_found, unresolved[]} indicando quais nomes não tiveram correspondência. Para controle fino (variante específica do banco, gramagem da medida caseira etc.), ainda é recomendado chamar webdiet_food_search e enviar o "id" explicitamente. Para evitar duplicação na UI ("2 2 fatias (50g)"), use quantidade numérica em "quantidade" e medida_caseira SEM repetir esse número e SEM sufixo "(Xg)" — ex.: quantidade="2", medida_caseira="fatias", peso_gramas="50". O MCP também normaliza automaticamente se você enviar texto completo. Exemplo sem ids (o adapter resolve automaticamente; nomes pouco específicos podem não encontrar match): [{"nome":"Café da Manhã","horario":"07:00","alimentos":[{"nome":"Pão integral","quantidade":"2","medida_caseira":"fatias","peso_gramas":"60"}]}] Exemplo com ids (pula o auto-resolve — ideal quando você já escolheu a variante exata): [{"nome":"Almoço","horario":"12:00","alimentos":[{"id":"9153","nome":"Arroz branco cozido","quantidade":"4","medida_caseira":"4 colheres de sopa (100g)","peso_gramas":"100"},{"id":"9168","nome":"Feijão carioca cozido","quantidade":"2","medida_caseira":"2 colheres (50g)","peso_gramas":"50"}]}] O save retorna {ok, metodo, warnings[], raw, auto_resolved_foods}. warnings avisa sobre alimentos sem id e alimentos não encontrados no banco. [Flattened action: publish] Bulk support: accepts patient_ids, prescription_ids for batched execution.
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  • Create or update patients in Dietbox. Actions: create (Name required, uses POST), update (partial fields, uses PATCH — no need to send all fields). Gender: Masculino or Feminino (converted to boolean for the API). Observation field for notes. Endereço, dados civis e de acesso (Address, Number, Complement, Neighborhood, State, City, Cep, MaritalStatus, Occupancy, Expire) são suportados em create e update — use os mesmos nomes de campo retornados pelo dietbox_patient (get); o adapter mapeia internamente a divergência de nomes do endpoint de update. Phone / MobilePhone são normalizados automaticamente para E.164 em create e update (o endpoint update/PATCH /patients rejeita HTTP 400 "'Body Mobile Phone Value' is not in format 'E.164'" quando recebe número sem prefixo internacional). Envie "67991234567", "(67) 99123-4567" ou "+5567991234567" indistintamente — o adapter ajusta para "+5567991234567" antes de chamar a API. Para números não-brasileiros, envie já com "+{código}". For destructive removal use dietbox_patient_delete. [Flattened action: update] Bulk support: accepts patient_ids for batched execution.
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  • Lista os senadores atualmente afastados (fora de exercício). Retorna `{ count, senadores }`, cada item com `codigo`, `nome`, `nomeCompleto`, `partido`, `uf`, `foto` e `emExercicio` (sempre `false`). Não requer parâmetros. Use `codigo` em `senado_obter_senador` para o detalhe; para os senadores em exercício (e busca por nome) use `senado_listar_senadores`.
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  • Lista votações em comissões. O parâmetro `por` (padrão `comissao`) define o eixo da consulta: `por: comissao` → exige `siglaComissao`; lista as votações daquela comissão. `por: senador` → exige `codigoSenador`; lista os votos do senador em comissões (filtro opcional `comissao`). `por: materia` → exige `sigla`, `numero` e `ano` (ex.: PL 2630/2020); lista as votações da proposição em comissões (filtro opcional `comissao`). Em todos os casos aceita período opcional `dataInicio`/`dataFim` (YYYYMMDD) e retorna `{ por, ...contexto, count, votacoes }`, cada votação com `codigo`, `data`, `comissao`, `materia`, `descricao`, `resultado`, totais (`totalSim`/`totalNao`/`totalAbstencao`) e `votos` (senador, partido, voto). Sem paginação. Obtenha siglas via `senado_listar_comissoes`, `codigoSenador` via `senado_listar_senadores`; para votações no plenário use `senado_votos_materia`.
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  • Busca licitações do Senado por número exato (ex: `19/2018`) ou texto do objeto. Retorna `{ count, total, licitacoes }` com os registros brutos da API administrativa, limitados a `limite` (padrão 50, máx 500). Exige ao menos `numero` ou `objeto` (sem filtro retorna erro). Para o contrato resultante de uma licitação, use `senado_contratos`.
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  • Lista emendas parlamentares dos senadores ao orçamento da União (e os ofícios de apoio a elas), conforme `tipo` (padrão `emendas`). `tipo: emendas` → `{ tipo, count, emendas }`, cada item com `codigo`, `numero`, `ano`, `tipo`, `autor`, `valor` e `descricao`. `tipo: oficios` → `{ tipo, count, oficios }`, cada item com `codigo`, `numero`, `data`, `tipo`, `descricao` e `situacao` (ofícios de apoio às emendas). Não recebe outros parâmetros; `count` é 0 e a lista vem vazia quando não há registros. Use para as emendas dos parlamentares ao orçamento federal — para a execução do orçamento interno do próprio Senado (despesas/receitas) use `senado_execucao_orcamentaria`.
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  • Search Chinese apparel industrial clusters and textile markets. USE WHEN user asks: - "where is China's [denim / suit / women's wear / underwear] manufacturing concentrated" - "what is the largest [silk / cashmere / down jacket] industrial cluster in China" - "industrial cluster comparison Humen vs Shaoxing vs Haining vs Zhili" - "recommend an industrial cluster for sourcing [product]" - "where should I set up a sourcing office for [category]" - "list mega clusters for [category]" - "fabric markets in Zhejiang / Jiangsu" - "accessories / trim / zipper / button markets in China" - "which province dominates [category] exports" - "follow-up: 'tell me more about Humen's cluster scale'" - "服装产业带 / 面料市场 / 产业集群 / 纺织集群 / 辅料市场" - "做 [品类] 应该去哪个产业带 / 集群推荐" Famous clusters this database covers include: Humen (Guangdong, womenswear), Shaoxing Keqiao (Zhejiang, fabric mega-market), Haining (Zhejiang, leather), Zhili (Zhejiang, children's wear), Shengze (Jiangsu, silk), Shantou (Guangdong, underwear), Puning (Guangdong, jeans), Jinjiang (Fujian, sportswear), and more. Returns paginated cluster list with name, location, specialization, scale, supplier count, average rent and labor cost, and key advantages/risks. WORKFLOW: Cluster discovery entry point. search_clusters → compare_clusters (side-by-side up to 10 cluster_ids) OR get_cluster_suppliers (list factories in that cluster) OR analyze_market (broader market view). RETURNS: { has_more: boolean, data: [{ cluster_id, name_cn, name_en, type, province, city, specialization, scale, supplier_count, labor_cost_avg_rmb }] } EXAMPLES: • User: "Where are the biggest denim clusters in China?" → search_clusters({ specialization: "denim", scale: "mega" }) • User: "Show me fabric markets in Zhejiang" → search_clusters({ province: "Zhejiang", type: "fabric_market" }) • User: "童装产业带有哪些" → search_clusters({ specialization: "童装" }) ERRORS & SELF-CORRECTION: • Empty data array → try in order: (1) drop scale filter, (2) broaden specialization (e.g. "服装" instead of "牛仔"), (3) remove type, (4) remove province. • Specialization mismatch → both Chinese and English work. Synonyms: sportswear/运动服, womenswear/女装, underwear/内衣, denim/牛仔. • Rate limit 429 → wait 60 seconds; do not retry immediately. • Empty after 3 retries → tell user: "No clusters match [criteria]. Try broader specialization or removing filters." AVOID: Do not use this for specific factory search — use search_suppliers. Do not compare clusters by calling search_clusters twice — use compare_clusters with cluster_ids. NOTE: Source: MRC Data (meacheal.ai). 170+ clusters mapped across 31 provinces. 中文:搜索中国服装产业带和面料市场。
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  • Retorna os detalhes publicos de um imovel especifico pelo id retornado por uma busca. Inclui fotos, descricao, caracteristicas (quartos, suites, vagas, area), endereco aproximado, preco anunciado e imobiliaria responsavel. Nao calcula financiamento nem avalia elegibilidade em programa de credito.
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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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