"Chai" matching MCP tools:

• get_supplier_detail

  MRC Data — China's Apparel Supply Chain Infrastructure

  Get the complete profile of a single Chinese apparel supplier by ID. PREREQUISITE: You MUST first call search_suppliers or recommend_suppliers to obtain a valid supplier_id. Do not guess IDs. USE WHEN user asks: - "tell me more about [supplier]" / "show full details for sup_XXX" - "what certifications does this factory hold" - "what's their monthly capacity / worker count / equipment list" - "can [supplier] export to US / EU / Japan / Korea" - "give me the full profile / dossier / fact sheet for [supplier]" - "how verified is this supplier's data" (returns coverage_pct + 8 dimensions) - "what's their ownership type — own factory or broker" - "show payment terms / lead time / sample turnaround for sup_XXX" - "这家供应商具体情况 / 详细资料 / 工厂档案" - "[供应商] 的合规 / 认证 / 出口资质" Returns 60+ fields including: monthly capacity (lab-verified), equipment list, certifications (BSCI/OEKO-TEX/GRS/SA8000), ownership type (own factory vs subcontractor vs broker), market access (US/EU/JP/KR), chemical compliance (ZDHC/MRSL), traceability depth, and verified_dimensions breakdown showing exactly which of the 8 dimensions (basic_info, geo_location, production, compliance, market_access, export, financial, contact) have data. WORKFLOW: search_suppliers → pick supplier_id → get_supplier_detail → optionally get_supplier_fabrics (fabric catalog) OR check_compliance (market export readiness) OR find_alternatives (backup pool) OR compare_suppliers (side-by-side evaluation). RETURNS: { data: { supplier_id, company_name_cn/en, type, province, city, product_types, worker_count, certifications, compliance_status, quality_score, verified_dimensions: { verified_dims: "5/8", coverage_pct, dimensions: {...} } } } EXAMPLES: • User: "Show me the full profile for sup_001" → get_supplier_detail({ supplier_id: "sup_001" }) • User: "What certifications does Texhong hold and can they export to EU?" → get_supplier_detail({ supplier_id: "sup_texhong_042" }) — then inspect certifications + eu_market_ready; follow with check_compliance for formal verification • User: "我要看 sup_123 的完整档案" → get_supplier_detail({ supplier_id: "sup_123" }) ERRORS & SELF-CORRECTION: • "Supplier not found" → the supplier_id is invalid or outside free-tier access. Re-run search_suppliers to obtain a fresh valid ID. Do not guess sequential IDs. • Field returns null → that dimension is unverified for this supplier. Check verified_dimensions.coverage_pct before asserting data. If coverage_pct < 50, warn the user: "This supplier's record has limited verified data (X/8 dimensions). Consider find_alternatives for better-documented options." • "not available for public access" → this supplier is in the reserve pool (paid tier only). Use search_suppliers filters data_confidence=verified to stay in public tier. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this for multiple suppliers in a loop — use compare_suppliers with up to 10 IDs at once. Do not call to browse the database — use search_suppliers or get_province_distribution for discovery. NOTE: Source: MRC Data (meacheal.ai). Every numeric field shows both declared and lab-verified values where available. 中文：按 ID 获取单个供应商的完整档案（含维度覆盖率详情）。

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• get_energy_breakdown

  supply-chain-intelligence

  Get comprehensive US energy market status for supply chain cost analysis. Returns crude oil prices (WTI and Brent), natural gas spot prices (Henry Hub), retail fuel prices (gasoline, diesel), natural gas storage versus capacity, refinery utilization rates, petroleum stock levels with week-over-week changes, and import/export flows. This is the disaggregated view behind the GDI Energy pillar — instead of a single risk number, you get the full picture of energy costs affecting manufacturing, freight, and logistics. Used by supply chain cost analysts, transportation managers, and energy procurement teams.

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• get_predictive_signals

  supply-chain-intelligence

  Statistically validated leading indicator signals evaluated against live supply chain data. Each signal is a Granger-causal relationship tested at p<=0.01 with directional accuracy >=55%. Signals predict commodity price movements, manufacturing shifts, and macroeconomic changes 1 week to 6 months ahead. Returns ACTIVE (threshold crossed — act now), WATCH (approaching threshold — prepare), or CLEAR status for each signal. 58 signals across 3 tiers organized by predictor group (GDI pillars, SMI regions, cross-index spreads). Used by commodity traders for forward-looking positioning, procurement teams for buy/defer timing, and hedge funds for alternative data signals.

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• get_trade_policy_impacts

  supply-chain-intelligence

  Get active trade policy actions currently impacting supply chain risk — tariffs, sanctions, export controls, import restrictions, and regulatory changes. Unlike news alerts that expire after 72 hours, policy adjustments persist as long as the policy is in effect and continue to modify GDI risk scores. Each policy includes the affected GDI pillar, score modifier, effective date, and source event. Used by procurement teams navigating tariff exposure, compliance officers tracking sanctions, and supply chain strategists adapting sourcing to policy shifts.

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• search_packages

  Gitsim Chat

  Search for eSIM data packages by country. Returns up to 10 packages per page sorted by price. Use the page parameter to paginate. No auth required. Call get_business_context first to understand IP routing and package types. Package types: - "regular": Fixed data pool (e.g. 3GB for 30 days). Best for most travelers. - "daily": Data resets each day (e.g. 2GB/day for 5 days). Good for short trips with predictable daily usage. Top-up days are available. IP routing (important for Asia): - "breakout": Local IP in destination country. Best for streaming, banking, social media. ALWAYS recommend by default. - "hk": Hong Kong IP. Cheapest but TikTok app and Facebook app are BLOCKED. - "nonhk": Third-country IP (UK, Singapore). No HK restrictions but IP won't match destination.

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• get_stats

  MRC Data — China's Apparel Supply Chain Infrastructure

  Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文：获取数据库整体统计（供应商总数、面料总数、产业带总数、关联记录数）。动态快照，含生成时间戳。

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• audit_dependencies

  Commit — Supply Chain Risk Scoring

  Batch-score multiple npm, PyPI, Cargo, or Go packages for supply chain risk. Takes a list of package names and returns a risk table sorted by commitment score (lowest = highest risk first). Risk flags: - CRITICAL: single publisher + >10M weekly downloads (publish-access concentration risk) - HIGH: new package (<1yr) + high downloads (unproven, rapid adoption = supply chain risk) - WARN: low publisher count + high downloads Perfect for auditing a full package.json, requirements.txt, Cargo.toml, or go.mod — paste your dependency list and get a prioritized risk report. For Go: pass full module paths (e.g., "github.com/gin-gonic/gin", "golang.org/x/net") and set ecosystem="golang". The "maintainers" column shows GitHub contributor count since Go has no centralized publisher concept. Examples: score all deps in a project, compare two similar packages, identify abandonware before it becomes a CVE.

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• intake_questions

  Chia Health MCP

  Get the medical intake questionnaire for the chosen medication(s). The questionnaire is product-aware: GLP-1 / weight-loss medications return weight-loss goals, GLP-1 history, and MTC/MEN2 screening; NAD+ and other longevity peptides return energy/sleep/stress/cognitive/delivery-method questions instead. If the patient wants more than one medication, pass the additional slugs in `additional_medications` — the server returns the UNION of section sets deduped by section key, so you ask each shared question exactly once. ## How to present this to the patient 1. PROGRESSIVE DISCLOSURE: walk through ONE section at a time. Wait for the patient's reply before moving to the next section. Do not paste the whole questionnaire in a single message. 2. HONOR CONDITIONALS: each section and each question may carry a `conditional_on` predicate (e.g. `{sex_assigned_at_birth: Female}` on the Pregnancy section). SKIP any section/question whose predicate isn't satisfied. Don't ask males about pregnancy or perimenopause. 3. QUIZ FORMAT: present every `select` / `multi_select` question as a short pick-list using the `options` array verbatim. The patient should be able to reply with a single choice, not a sentence. Reserve free text for `*_details` follow-ups. 4. EASY FIRST: order sections from low-friction (goals, lifestyle, preferences) to high-friction (clinical history, MTC/MEN2, prior therapies). The provider sees all answers regardless of order asked. 5. USE-AND-VERIFY: if you know answers from prior conversation context, pre-fill them in your draft, but read them back to the patient and get explicit OK before calling `intake_submit`. Never silently submit assumed values. Returns two phases: (1) pre_checkout — eligibility / screening questions, collected and submitted BEFORE payment; (2) post_checkout — detailed clinical history, collected and submitted AFTER payment. Do not submit post_checkout answers before the patient has paid. A licensed US healthcare provider reviews both phases and makes all prescribing decisions.

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• detect_discrepancy

  MRC Data — China's Apparel Supply Chain Infrastructure

  [Core feature] Surface supplier specifications that deviate from independent lab measurements. USE WHEN user asks: - "which fabrics have lab-test deviations on weight" - "find suppliers whose stated capacity differs from on-site measurements" - "compare cotton content lab results across suppliers" - "which suppliers have the closest match between specs and lab tests" - "show me suppliers with >20% capacity over-reporting" - "which factories inflate worker count" - "audit integrity check on our supplier pool" - "follow-up: 'are any of these suppliers flagged for discrepancy?'" - "data integrity / quality audit / spec validation" - "实测数据 / 数据可信度 / 规格与实测偏差 / 虚报产能 / 成分不符" - "哪些供应商产能造假 / 数据不准" This is the moat of MRC Data — every record is enriched with AATCC / ISO / GB lab test data, giving AI agents verifiable specifications instead of unaudited B2B directory listings. Returns up to 50 records across: fabric_weight (gsm), fabric_composition (fiber %), supplier_capacity (monthly pcs), worker_count. Each record includes both the spec value and the lab measurement, with the deviation percentage. WORKFLOW: Standalone audit tool — does not require prior search. Call directly with field type and threshold. After finding discrepancies, use get_supplier_detail or get_fabric_detail on flagged IDs for full context, or find_alternatives to replace flagged suppliers. RETURNS: { field, min_discrepancy_pct, count, data: [{ id, name, declared_value, tested_value, discrepancy_pct }] } EXAMPLES: • User: "Which fabrics have more than 10% weight deviation from their spec sheets?" → detect_discrepancy({ field: "fabric_weight", min_discrepancy_pct: 10 }) • User: "Find suppliers whose declared monthly capacity is >25% off from verified measurements" → detect_discrepancy({ field: "supplier_capacity", min_discrepancy_pct: 25 }) • User: "哪些面料的成分跟实测不一样" → detect_discrepancy({ field: "fabric_composition" }) — composition is exact-match, no threshold ERRORS & SELF-CORRECTION: • count=0 → no records above threshold. Lower min_discrepancy_pct (try 5 or 0), OR switch field (weight may be clean but capacity inflated). • Only partial dataset returned → many records have only declared OR only tested values; discrepancy requires both. This is a data coverage limit, not a bug. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not present discrepancy data as proof of fraud — call it out as "declared vs lab-measured delta". Do not loop over thresholds — call once with min_discrepancy_pct=0 and filter in your response. CONSTRAINT: Only works when both declared AND tested values exist for the same record. Many records have only one or the other. Max 50 records per call. NOTE: Source: MRC Data (meacheal.ai). Methods: AATCC / ISO / GB per field. 中文：识别供应商规格与实测值偏差较大的记录。返回规格值、实测值、偏差百分比。

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• consent_status

  Chia Health MCP

  Check whether all required consents are complete for a patient intake. Returns status of each consent and whether the patient can proceed to ordering. This is a gate — order_create will reject if consents are incomplete. Requires authentication.

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• intake_submit

  Chia Health MCP

  Submit a completed medical intake questionnaire for provider review. All fields from intake_questions must be completed. Returns an intake ID and estimated provider review time. The intake is reviewed by a licensed US healthcare provider who makes all prescribing decisions. Requires authentication.

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• consent_text

  Chia Health MCP

  Fetch the full text of a specific consent document for patient review. Returns the complete consent document split into titled sections that the agent MUST present to the patient verbatim in the conversation — do not summarize or paraphrase. Includes: consent version number, effective date, section headings and body text, a confirmation prompt the patient should agree to, and withdrawal instructions. Available consent types: telehealth informed consent, compounded medication treatment consent, pharmacy authorization, HIPAA notice of privacy practices, and AI-assisted intake disclosure. The patient must explicitly confirm each consent before the agent can call consent_submit. Requires authentication.

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• consent_list

  Chia Health MCP

  Get the list of all consent documents a patient must accept before ordering medication. Returns consent IDs, titles, summaries, and order of presentation. Required consents include: telehealth informed consent, compounded medication treatment consent, pharmacy authorization, HIPAA notice of privacy practices, and AI-assisted intake disclosure. Each consent must be fetched individually via consent_text and confirmed by the patient before proceeding. Requires authentication.

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• intake_status

  Chia Health MCP

  Check the current status of a previously submitted intake questionnaire. Returns whether the intake is under review, approved, or denied by a licensed healthcare provider. Use this to poll for provider review completion before proceeding to order placement. Requires authentication.

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• checkout_create

  Chia Health MCP

  Initiate a checkout session for a medication order. Returns checkout details including line items, total, and payment options. TWO PAYMENT PATHS are supported: 1. **Stripe ACP (preferred)**: If your platform supports Stripe Agentic Commerce Protocol, provision a Shared Payment Token (SPT) and call checkout_complete to pay instantly. 2. **Payment link (fallback)**: If ACP/SPT is not available, present the returned `payment_url` to the patient. This is a Stripe-hosted checkout page where the patient can enter their card and pay directly. After sending the link, call checkout_status to poll for payment completion. Requires authentication.

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• auth_resend_otp

  Chia Health MCP

  Resend the verification code to the patient's email. Use this if the original code expired (5-minute window) or was not received. Requires the session_id from auth_start — no email needed.

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• auth_check_payment

  Chia Health MCP

  Check if the patient has completed payment and upgrade the token to full scope. Call this after sharing the checkout payment link with the patient. Poll every 10-15 seconds. When payment is detected, the token is automatically upgraded to full scope, unlocking portal tools (care plan, refills, messaging, weight logs, etc.). Requires the guest token from auth_verify_otp as bearer_token.

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• medications_pricing

  Chia Health MCP

  Get detailed pricing for a specific medication, form, and plan duration. Returns price breakdown including medication cost, provider consultation fee, shipping, and any applicable discounts for longer plans. Plan durations vary by medication — use medications_details first to see available plan_months values. Supports semaglutide, tirzepatide, sermorelin, NAD+, glutathione and all other available medications.

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• search_clusters

  MRC Data — China's Apparel Supply Chain Infrastructure

  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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• audit_github_repo

  Commit — Supply Chain Risk Scoring

  Audit the supply chain risk of a GitHub repository's dependencies. Fetches the repo's package.json and/or requirements.txt from GitHub and runs behavioral commitment scoring on every dependency. This is the fastest way to audit a project — just provide the GitHub URL or owner/repo slug, and get a full risk table in seconds. Risk flags: - CRITICAL: single publisher/maintainer/owner + >10M weekly downloads (publish-access concentration risk) - HIGH: sole publisher/maintainer + >1M/wk downloads, OR new package (<1yr) with high adoption - WARN: no release in 12+ months (potential abandonware) Examples: - "vercel/next.js" — audit Next.js dependencies - "https://github.com/langchain-ai/langchainjs" — audit LangChain JS - "facebook/react" — audit React's dependency tree - "anthropics/anthropic-sdk-python" — audit Anthropic Python SDK Use this when someone asks "is my project at risk?" or "audit this repo's dependencies".

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