"Bambu Lab" matching MCP tools:

• mix_colors

  rnv-color-mcp

  Blend up to 12 colors into one. Each color may be a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold, near-black), or a saved-palette reference (Spring line, or 'Spring line:2' for its 2nd swatch). Optional integer weights bias the blend (defaults to equal). mode selects the model: rgb/hsv/lab are digital blends (lab is perceptual and the default, best for on-screen color); paint mixes pigments via Kubelka-Munk physics (colors darken like real paint, use it for physical-media matching); ryb is the artist's color wheel; cmy is subtractive like printer inks. Returns hex and rgb. Read-only and deterministic: it computes a result and stores nothing, so it is safe to call repeatedly with no side effects. Use to combine multiple colors into a single blend; to convert one color between formats use convert_color, and to measure how far apart two colors are use color_difference.

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• convert_color

  rnv-color-mcp

  Convert a color between formats. Input accepts a hex, CSS name, RNV brand name, or saved-palette reference. With `to` set to one of hex/rgb/hsv/hsl/lab, returns just that format; otherwise returns all of them. Read-only and deterministic, with no side effects. Use for format conversion of a single color; to blend several colors into one use mix_colors, and to compare two colors use color_difference.

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• 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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• search_within

  Data La

  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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• log_lab_results

  Wellness Project

  Log one or more blood test or biomarker results. Use when the user shares lab values — copy-pasted from a Quest/LabCorp PDF, typed from a paper report, or described from a photo of their results. REQUIRED WORKFLOW: 1) call list_lab_markers for canonical names and LOINC codes. 2) for each marker the user provides, find the best match and use its canonical marker_name and loinc_code. 3) if no match exists, use the name as stated and omit loinc_code. If the user says they have lab results but hasn't shared them, prompt: "You can paste the text from your lab report PDF, or upload a photo of the results page — I'll parse all the values at once." INFER — do not ask: date (look for a collection/drawn date in the pasted text, default today), panel_name (from list_lab_markers for matched markers, infer for unmatched), flag (extract from the report if present: "H", "L", "HH", "LL", "A"), ref_range_low/high (parse from the report if shown), lab_name (from the report header, same for all markers in a visit). Submit all markers from a single lab visit in one call.

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• update_meal

  ForkMate

  Correct a food already logged to the user's diary — fix a wrong calorie/macro value, quantity, or name, or move an entry to a different meal. Identify the entry by its `id` and `local_date` (both from get_day) and the food by its `item_index` within that entry's items[]. Only the fields you send change; the macros you send are MERGED onto the existing ones (so sending just `kcal` leaves protein/carb/fat as they were). This overwrites the value IN PLACE — there is no history of the previous value. Editing never moves an entry to another day (to do that, delete and re-log). SAFETY: all calorie and macro values here — including carbohydrates — are ESTIMATES (from USDA / Open Food Facts or the user's own entry), approximate and not lab-measured or per-batch. They are for general nutrition tracking ONLY. Do NOT use them for insulin dosing, carb-counting for a bolus, blood-glucose prediction, or any other medical decision, and do NOT compute a dose or glucose estimate from them. For anything medical, direct the user to the product's own label and their care team.

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• create_checkout

  La Luer — AI Skincare Commerce

  Create a checkout URL for one or more products. Pass variant IDs (items) and/or product URLs (product_urls). When a product URL is provided (e.g. https://laluer.com/products/mira), the tool resolves it to a variant ID automatically — no catalog import needed. Supports discount codes, cart notes, and selling plans. Do not use unless the user wants to buy — use search_products or skincare_recommend first. Returns a direct Shopify checkout link the user can click to buy.

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• recent_changes

  Data La

  "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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• validate_claim

  Data La

  "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. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), the grounded or structured actual value with pipeworx:// citation, and reasoning. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

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• polymarket_kalshi_spread

  Data La

  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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• pipeworx_feedback

  Data La

  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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• la_query

  Data La

  Run a raw SoQL query against any Los Angeles open-data resource (data.lacity.org) by its Socrata id (8-char like "2nrs-mtv8"). Full SoQL: where/select/group/order/limit/offset. Use la_datasets to find a resource id, or la_recent for the common ones.

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• mix_colors

  rnv-color-mcp

  Blend up to 12 colors into one. Each color may be a hex (#d2bc93), a CSS name (red), an RNV brand name (brand gold, near-black), or a saved-palette reference (Spring line, or 'Spring line:2' for its 2nd swatch). Optional integer weights bias the blend (defaults to equal). mode selects the model: rgb/hsv/lab are digital blends (lab is perceptual and the default, best for on-screen color); paint mixes pigments via Kubelka-Munk physics (colors darken like real paint, use it for physical-media matching); ryb is the artist's color wheel; cmy is subtractive like printer inks. Returns hex and rgb. Read-only and deterministic: it computes a result and stores nothing, so it is safe to call repeatedly with no side effects. Use to combine multiple colors into a single blend; to convert one color between formats use convert_color, and to measure how far apart two colors are use color_difference.

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• colour_mix

  Colour Memory

  Simulate perceptually modelled subtractive mixing of two colours in CIE Lab space (not RGB screen blending). Returns the resulting mixed hex value and its nearest archive match with cultural context. Uses CIE Lab subtractive model for perceptual accuracy. Example: mixing Prussian Blue and Yellow Ochre gives a muted green — the tool identifies which archive colour that green most closely matches.

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• get_brand_identity

  Origine Paris

  Returns the identity of Origine Paris, the Parisian fine jewellery house of recycled 18ct gold and IGI-certified lab-grown diamonds. Use it for ready-to-use brand facts (trading name, legal identity (SIREN), descriptions, positioning, the by-appointment address at 21 rue de la Paix, contacts, official profiles); for the underlying source markup use get_jsonld_graph, and for the catalogue use search_catalogue, not this. Read-only and side-effect-free: it returns a structured identity object plus a text copy, with the sources, the index timestamp and the canonical URL, taken from the site JSON-LD and Wikidata and served as published; absent values are reported as "unknown", never invented.

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• get_person_profile

  Origine Paris

  Returns a detailed, sourced profile of one Origine Paris founder, the recycled gold and lab-grown diamond jewellery house. Use it for a single founder's biography, career with dates and references, roles, education and citizenship; for the two-person roster use get_founders instead. Provide exactly one of name or qid. Read-only and side-effect-free: it returns a structured profile object plus a text copy, with the sources, the index timestamp and the canonical URL, from Wikidata and the site JSON-LD; an unrecognised person yields an explicit "unknown" result, never a guess.

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• get_supplier_fabrics

  MRC Data — China's Apparel Supply Chain Infrastructure

  List all fabrics a specific supplier can provide, with quoted prices. USE WHEN user asks: - "what fabrics does [supplier name] have" / "what can this factory source for me" - "show me the catalog of supplier sup_XXX" - "what does this manufacturer offer" - "what fabric options does sup_XXX quote for denim" - "does [supplier] supply [fabric type]" - "price list / fabric catalog / offering sheet for sup_XXX" - "MOQ per fabric at this supplier" - "follow-up: 'what fabrics can they supply?' after identifying a supplier" - "[供应商] 能供应哪些面料 / 报价表 / 起订量" Returns fabric records linked to the supplier with: fabric name, category, weight, composition, and the supplier's quoted price + MOQ for that specific fabric. PREREQUISITE: You MUST have a valid supplier_id from search_suppliers or get_supplier_detail. WORKFLOW: search_suppliers → get_supplier_detail → get_supplier_fabrics → optionally get_fabric_detail (for lab-test data on a specific fabric) OR get_fabric_suppliers (cross-check price vs other suppliers for same fabric). RETURNS: { supplier_id, count, data: [{ fabric_id, name_cn, category, weight, composition, price_rmb, moq }] } EXAMPLES: • User: "What fabrics does sup_texhong_042 offer?" → get_supplier_fabrics({ supplier_id: "sup_texhong_042" }) • User: "Show me the fabric catalog and MOQs for sup_001" → get_supplier_fabrics({ supplier_id: "sup_001" }) • User: "sup_234 能做哪些面料，报价多少" → get_supplier_fabrics({ supplier_id: "sup_234" }) ERRORS & SELF-CORRECTION: • count=0 → this supplier has no linked fabric catalog in the database. Either (a) they don't self-source fabrics (CMT-only) — confirm via get_supplier_detail.ownership_type, or (b) their catalog is unmapped — use search_fabrics with their expected specialization instead. • "Supplier not found" (implicit) → the supplier_id is invalid. Re-run search_suppliers. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this for a general fabric search — use search_fabrics. Do not call to compare prices across suppliers for the SAME fabric — use get_fabric_suppliers instead. NOTE: Source: MRC Data (meacheal.ai). Prices are supplier-quoted, not binding offers. 中文：查询某供应商能供应的所有面料及其报价、起订量。

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• compare_entities

  Data La

  "Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

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• polymarket_fill_risk

  Data La

  Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of `market` (single-market mode) or `event` (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

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• convert_color

  rnv-color-mcp

  Convert a color between formats. Input accepts a hex, CSS name, RNV brand name, or saved-palette reference. With `to` set to one of hex/rgb/hsv/hsl/lab, returns just that format; otherwise returns all of them. Read-only and deterministic, with no side effects. Use for format conversion of a single color; to blend several colors into one use mix_colors, and to compare two colors use color_difference.

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