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"namespace:io.github.platfone-com" matching MCP tools:

  • What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).
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  • Population d'une COMMUNE (code INSEE 5 car.), d'un DÉPARTEMENT (2-3 car.) OU d'un IRIS infracommunal (9 car.) — granularité auto-détectée par la longueur du `code`. Retourne un `LookupResult` discriminé par `found`. - IRIS (9 car., ex `751103701` = commune `75110` + IRIS `3701`) : population totale du quartier au Recensement 2022 (champ `population`, comptes bruts), + `libelle`, `code_commune`, `type_iris` (H/A/D/Z). Source : INSEE RP 2022 (table ingérée, géo 01/01/2024). Maille la plus fine (quartier) pour les villes ; en zone peu dense la commune = 1 IRIS (`type_iris` Z, code `COM+0000`). Pour le profil démographique détaillé d'un îlot ou d'un bassin (âge, CSP, familles, revenu), utiliser `profil_iris`. - Commune (5 car., ex `75056` Paris, `13055` Marseille, `2A004` Ajaccio) : PMUN/PCAP/PTOT. Source INSEE Melodi (DS_POPULATIONS_REFERENCE). PMUN = base légale DREES. Commune fusionnée → `found: false` + orientation `autocomplete_commune`. INSEE n'expose PAS les arrondissements PLM (75101-75120, 13201-13216, 69381-69389) → passer la commune-mère ou le département. - Département (2-3 car., ex `75`, `59`, `2A`, `971`) : Mayotte (`976`) ABSENTE de Melodi → `lookupNotFound`. Alias acceptés : `code_insee`/`codeInsee`/`insee`, `code_dept`/`dept`/`departement`/`code_departement`, `code_iris`/`iris` → `code`.
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  • Parse a Primavera P6 XER file and return a TABLE SUMMARY (not the full row-level data — XER row dumps explode the MCP context window). For each table in the XER, returns the table name, field list, and record count. Per-row data is intentionally omitted — for forensic / DCMA / windows analysis use the dedicated tools (``forensic_windows_analysis``, ``critical_path_validator``, etc.) which consume the parsed XER internally and return analytical summaries, not raw rows. Use this tool to confirm an XER is parseable, list its tables, see the data date / project name from PROJECT, or count activities in TASK before deciding which deeper tool to run. Args: xer_path: server-side filesystem path to the XER file. xer_content: full text of the XER file (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. Returns: { "filepath": absolute path, "encoding_used": "utf-8" | "cp1252" | ..., "ermhdr": file header dict (P6 version, export user, etc.), "tables": [{"name", "fields", "record_count"}, ...], "table_count": int, "total_records": int, "project_summary": { "proj_id", "proj_short_name", "proj_long_name", "data_date", "plan_end_date" } (from first PROJECT row, if any) }
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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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  • 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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  • "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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  • Virtual phone numbers for AI agents — rent numbers in 200+ countries, receive SMS.

  • Virtual phone number platform for AI agents — rent numbers across 200+ countries, receive SMS, and manage the full activation lifecycle.

  • REQUIRES one of `event` (single-event mode) OR `topic` (cross-event mode) — call with no args fails. Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. `event` (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). `topic` (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}.
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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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  • 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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  • Semantic search INSIDE a fetched record. Pass the text you already pulled (e.g. a SEC 10-K body, an article, a long tool result) plus a natural-language query; get back the top-N passages with character offsets and similarity scores. Use when the record is too big to cram into the prompt — search_within saves context, returns only the passages that matter, and every passage carries an offset so the agent can verify a verbatim quote. Pairs with ask_pipeworx_grounded: fetch with the gateway, ground over the relevant passages instead of the whole document. BGE-base-en embeddings + cosine over 500-char overlapping windows; cap is 200K chars (longer inputs are truncated and flagged).
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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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  • "Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
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  • Monte Carlo Schedule Risk Analysis — P10/P50/P80/P90 completion-date forecast for a Primavera P6 schedule. Implements an AACE-style quantitative SRA (the same math as CPP's browser Tool_11 Portfolio Risk Engine, scripted Python counterpart). For each iteration, every activity duration is sampled from the chosen distribution (Triangular, BetaPERT, Uniform, Lognormal, etc.) parameterized by % of baseline duration; CPM re-runs and the project finish date is recorded. After all iterations, P10/P50/P80/P90 completion dates and a sensitivity tornado (per-activity correlation to project finish) are reported. Use this tool when you need probabilistic completion forecasts or a tornado/sensitivity ranking. For the AACE 122R-22 QRAMM maturity badge on the result, pipe the response into ``qramm_maturity``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. iterations: number of MC iterations (default 5000). distribution: 'Triangular', 'BetaPERT', 'Uniform', 'Lognormal' (case-insensitive — passed through). optimistic_pct, most_likely_pct, pessimistic_pct: % of baseline duration for the distribution params (defaults: 85 / 100 / 120). seed: optional fixed seed for reproducibility (0 = system entropy = non-reproducible). output_dir: optional output dir; tempdir if "". Returns: Full SRA result dict, key paths: - 'baseline.percentiles': {'P10', 'P50', 'P80', 'P90'} - 'baseline.config': sim params used - 'baseline.sensitivity': per-activity tornado rows - 'project_name', 'data_date', ... - HTML / DOCX paths if outputs emitted
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  • Latest glucose reading for a patient (value, trend, flags). For history use librelink_business_get_glucose_graph. Read-only CGM data — clinic/follower account; not for medical decisions without clinician review. Bulk support: accepts patient_ids for batched execution.
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  • Cria um produto (nome + valor_venda obrigatórios em `data` JSON). A API v2 do Conta Azul não expõe atualização nem GET de produto por id — para consultar, use contaazul_list_products (/produto/busca) com filtro.
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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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  • 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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  • 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: create] Bulk support: accepts patient_ids, prescription_ids for batched execution.
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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: save] Bulk support: accepts patient_ids, prescription_ids for batched execution.
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  • Collapsed As-Built / But-For analysis on a post-impact XER. Implements AACE RP 29R-03 §3.8 Modeled / Subtractive / Single Base method (paired with MIP 3.3 Windows for the dual-method gap report per SCL §11.5). Validates a forensic windows analysis (MIP 3.3) by independently computing the same project drift via subtractive removal of delays from the as-built schedule. For each delay event, the as-built duration of every ``affected_activity`` is shortened by ``impact_days`` (or removed entirely if ``removal_method="remove"``), then CPM re-runs and the resulting "but-for" finish date is compared to the as-built finish. Cumulative pass removes ALL events at once for a project-level but-for finish. Use this tool when opposing counsel demands a but-for analysis or you need a dual-method validation pairing §3.3 (windows) with §3.8 (collapsed-as-built). For prospective fragnet insertion (MIP 3.7), use ``time_impact_analysis_fragnet`` instead. Args: as_built_xer_path: server-side post-impact XER (after delays incurred). as_built_xer_content: full text of post-impact XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. delay_events: list of event dicts. Each must have ``event_id``, ``affected_activities`` (list of task_codes), and ``impact_days`` (number). Optional: ``removal_method`` ('shorten'|'remove'), ``responsible_party``, ``name``, ``description``. output_dir: optional output dir for HTML/CSV (tempdir if ""). project_name: optional override. removal_method: global default 'shorten' or 'remove'. contractor_filter: when True, exclude contractor-caused events from the cumulative pass (owner audit mode). Returns: { "as_built_finish": "YYYY-MM-DD", "per_event_results": [{event_id, but_for_finish, impact_days, ...}, ...], "cumulative_but_for_finish": "YYYY-MM-DD", "cumulative_impact_days": int, "dual_method_gap": dict | None, "output_files": {...}, "warnings": [...], "method": "AACE 29R-03 §3.8 (Modeled/Subtractive/Single Base)" }
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