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  • Validate whether a component will work within your operating conditions. Compares your design parameters against the datasheet's absolute maximum ratings and recommended operating conditions. Returns PASS/FAIL/WARNING per parameter with margin percentages. Parameter mapping by component type: - Buck/boost converters: input_voltage, output_voltage, output_current, ambient_temp - MOSFETs: supply_voltage=VDS, output_current=ID (drain current), ambient_temp - LDOs: input_voltage, output_voltage, output_current, ambient_temp - Logic ICs: supply_voltage=VCC, ambient_temp Result semantics (per-parameter 'result' field): - PASS: comfortable margin. For a recommended-operating RANGE, any value inside the range (including the exact edges) is PASS. For an absolute-MAX rating, PASS means more than 10% below the limit. - WARNING: for absolute-max ratings only, the user value is within 10% of the limit (but not over) — part will work but with thin margin for part-to-part variation, temperature drift, and transients. Consider derating. - FAIL: user value is outside the allowed range / exceeds the limit — part is out of spec and will be stressed or damaged. - INSUFFICIENT_DATA: the check could not be completed safely — most commonly an ambient_temp input that could not be translated to a junction-temperature check (see the temperature note below). INSUFFICIENT_DATA never counts as a PASS: it pulls the overall verdict down to at-least-WARNING. Each check also reports 'limit_type' (max / min / range / fixed) so you can see whether it was judged against an operating range, an absolute-max rating, or a fixed-output setpoint. Temperature honesty: - ambient_temp is NOT silently treated as a junction temperature. The stored operating-temperature limit is junction-suspect, so a bare ambient input is never reported as a clean PASS on thermal grounds. If you also supply input_voltage, output_voltage and output_current AND a thermal-resistance (RthJA) value is available, the tool estimates Tj = Ta + Pd·RthJA (Pd ≈ (Vin-Vout)·Iout, a documented approximation) and checks the junction estimate — reported as 'temperature (junction est.)' with the assumptions in the note. Otherwise the temperature check returns INSUFFICIENT_DATA asking you to pull RthJA via read_datasheet. The 'temperature_basis' field (junction / ambient / unknown) tells you which basis was used. Behavior: - Two-tier validation. For parameters in our structured database (Vin, Iout, operating temp, etc.), returns instantly and free of LLM cost. For parameters only found in the datasheet text, falls back to an LLM read of the absolute-max and recommended-operating-conditions sections. The 'validation_method' field in the response tells you which path was used. - If the part hasn't been extracted yet and the LLM fallback is needed, this call triggers extraction (30s-2min). Returns status='extracting' if so — poll check_extraction_status and retry. When NOT to use: - You need power dissipation or junction-temperature rise — this tool only checks nameplate limits. Pull RthJA from read_datasheet and calculate yourself. - You need SOA (safe-operating-area) curve checks for MOSFETs — use analyze_image on the SOA graph. - You're checking a passive or mechanical part with no abs-max table — there's nothing for this tool to compare against. Example: check_design_fit('TPS54302', input_voltage=24, output_current=2.5, ambient_temp=70)
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  • Search VFB terms. This is the search virtualflybrain.org itself runs — the same Solr query, the same ranking — so what comes back first here is what a user would see first on the site. USE filter_types BY DEFAULT. Unfiltered searches mix scRNAseq artifacts and developmental stages in with the entity the user wants. Common filter_types recipes: - Neuron classes: ["neuron", "class"] - Individual neurons with images: ["neuron", "has_image"] - Neurons with connectome data: ["neuron", "has_neuron_connectivity"] - Brain regions / neuropils: ["anatomy"] - Genes: ["gene"] - Driver lines / expression patterns: ["expression_pattern"] - Datasets: ["dataset"] There are over 200 type names and they change as data is added, so do NOT guess them: call list_search_facets to see the current vocabulary (optionally filtered, e.g. contains="lineage"). Names are matched case- and separator-insensitively, and a name that does not exist is an error with suggestions rather than a silently empty result. Deprecated terms are excluded by the search itself — you do not need exclude_types: ["deprecated"], and adding it is harmless but pointless. Stage filtering: VFB covers adult, larval, and embryonic data, and many anatomical FBbt classes are stage-agnostic. Do NOT add "adult" or "larva" to filter_types by default — only add them when the user is explicit about a stage (e.g. "adult Kenyon cells", "larval mushroom body"). Default searches should leave stage out so stage-agnostic classes and all life stages are visible. Useful flags: - unique=true (the default) → one row per term. Turn it OFF only when you need to see WHICH synonym matched; with unique=false a term appears once per matching synonym, so "Kenyon cell" can return the same ID several times. - minimize_results=true → top 10, essential fields only, for exploratory searches. - auto_fetch_term_info=true → if an exact label match is found, returns get_term_info in the same response. - boost_types=["has_image", "has_neuron_connectivity"] → float data-rich entities to the top of the list without excluding anything else. - demote_types=["expression_pattern_fragment"] → sink noisy types to the bottom of the list instead of removing them. If the search returns no good matches, do NOT fall back to training-data answers — try alternative spellings, synonyms, broader terms, or different filter_types. Multiple filter_types are ANDed (results must match ALL). Multiple exclude_types are ORed (any match excludes). boost_types and demote_types re-order without excluding; boost wins if a term matches both.
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  • Composite: take a natural-language intent, fan out parallel scoped searches across the bundled docs for all four DERO products (derod, tela, hologram, deropay), boost any product_hint matches by 1.5×, and return a ranked recommendation list with per-result rationale plus ready-to-cite related_docs. When to call: at the START of any "where do I read about X?" or "which docs cover Y?" investigation, BEFORE calling dero_docs_search directly. PREFER this over guessing the right product: this composite already runs all four products in parallel, dedupes overlap, surfaces the top heading per result as rationale, and gives you the top-2 citations pre-built. Pass product_hint when the user has already said e.g. "TELA" or "DeroPay" so that product's matches float to the top. Input Requirements: - `intent` is REQUIRED. Free-text description of what the user is trying to do (min 8 chars). Drop verbs and use product nouns like "deploy a TELA app" or "verify a DeroPay webhook signature" for best results. - `product_hint` is OPTIONAL. One of `derod | tela | hologram | deropay`. Multiplies hint-product scores by 1.5×. - `limit_per_product` is OPTIONAL (default 2, max 5). Cap per-product hits before merging. Output: `{ intent, product_hint, limit_per_product, recommended: [{ product, slug, title, canonical_url, score, boosted_score, rationale }], by_product: { derod | tela | hologram | deropay: { count, top_slug, top_score } }, related_docs: DeroCitation[] }`. `related_docs` is the top-2 picks pre-built as citations the agent can drop straight into a response. On zero matches across every product the composite returns a structured `_meta.error` with code `NO_DOCS_MATCH` and a hint to rephrase or drop the product_hint.
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  • Discover Japanese train stations by describing what you want around them, in English or Japanese — "朝ラーメンが食べられて車椅子トイレがある駅", "terminal station with late-night ramen", "水害リスクが低くてラーメンが多い駅". Semantic search over 9,035 station profiles (lines/terminal size, ramen density & styles, in-station accessible-toilet equipment, official hazard categories, ridership) with hybrid metadata filters — the filters guarantee the constraint, the embedding ranks by fit. Filter intent in the query text (朝ラー/深夜/おむつ/車椅子/水害リスク低…) is auto-applied (filter_source: inferred); explicit params win. Water-hazard intent (水害/洪水/浸水/高潮…リスク低) expands to flood rank AND storm-surge zone; 液状化/地盤 intent filters on the official liquefaction-tendency category; results carry risk_notes when other official hazard categories are high. Inferred facility filters with partial data coverage (おむつ/車椅子 — Tokyo-only data) BOOST confirmed stations instead of excluding unknowns (see soft_filters); explicit params remain strict. Taste/quality words (うまい, "good food", delicious…) are not evaluated (no review data); ramen ranking reflects shop density and style variety only. name_contains gives exact substring matching on station names (日本語/romaji) when the name itself is the requirement. Coverage notes: toilet stats = Tokyo stations only; ridership = Greater Tokyo operators only; hazard = official MLIT categories relayed as-is, NOT a safety judgment. Role split: station_search finds candidate stations — then get_toilet_by_station / search_ramen / get_station_hazard / get_station_context for detail on one station.
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  • Discover Japanese train stations by describing what you want around them, in English or Japanese — "朝ラーメンが食べられて車椅子トイレがある駅", "terminal station with late-night ramen", "水害リスクが低くてラーメンが多い駅". Semantic search over 9,035 station profiles (lines/terminal size, ramen density & styles, in-station accessible-toilet equipment, official hazard categories, ridership) with hybrid metadata filters — the filters guarantee the constraint, the embedding ranks by fit. Filter intent in the query text (朝ラー/深夜/おむつ/車椅子/水害リスク低…) is auto-applied (filter_source: inferred); explicit params win. Water-hazard intent (水害/洪水/浸水/高潮…リスク低) expands to flood rank AND storm-surge zone; 液状化/地盤 intent filters on the official liquefaction-tendency category; results carry risk_notes when other official hazard categories are high. Inferred facility filters with partial data coverage (おむつ/車椅子 — Tokyo-only data) BOOST confirmed stations instead of excluding unknowns (see soft_filters); explicit params remain strict. Taste/quality words (うまい, "good food", delicious…) are not evaluated (no review data); ramen ranking reflects shop density and style variety only. name_contains gives exact substring matching on station names (日本語/romaji) when the name itself is the requirement. Coverage notes: toilet stats = Tokyo stations only; ridership = Greater Tokyo operators only; hazard = official MLIT categories relayed as-is, NOT a safety judgment. Role split: station_search finds candidate stations — then get_toilet_by_station / search_ramen / get_station_hazard / get_station_context for detail on one station.
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  • List a new product on ProductClank as a token-free listing (no crypto/token, no wallet). At minimum pass a `url` — the server auto-fills the name, tagline, description, logo, and X handle from the site; any field you pass explicitly overrides what's extracted. Socials are optional. Use this when search_products finds no existing match and the user wants to run a boost or campaign for a product that isn't listed yet. Returns the new product's id (and reuses an existing listing if one already matches, rather than duplicating). FREE — no credits charged. Confirm the product details with the user before calling.
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  • AI audio tools for music producers — stem splitting, vocal removal, BPM & key detection, audio-to-MIDI, format conversion, trimming, video-to-audio extraction and AI song generation.

  • Warm CDN and reverse-proxy caches: manage sites, run warms, read cache-hit stats.

  • Semantic / vibe search over the same nationwide ramen DB — describe what you feel like eating in natural language, English or Japanese ("rich creamy pork broth", "あっさり淡麗な醤油", "oily mazesoba", "tsukemen near Ebisu station"), and get the closest shops by meaning, each with a similarity score. Powered by multilingual embeddings (bge-m3), so English queries find shops with Japanese-only names. Role split: use search_ramen for exact facts (shop name lookup, keito/prefecture/status filters, geo radius) — use vibe_search for descriptive/fuzzy queries where no exact filter fits. Style rankings reflect only classified shops (~25%); unclassified shops still match by name and place. Tip: concrete food words (style, broth, richness, place, hours) match far better than abstract mood words ("stylish", "hardcore") — translate moods into concrete attributes before querying. Prefecture intent in the query text (北海道, 博多の…) is auto-applied as a filter (pref_source: inferred); region-style names (札幌ラーメン, 喜多方, 佐野…) stay pure style words and never restrict location. Dish-concept words (オロチョン, カラシビ, 台湾ラーメン/まぜそば, 勝浦タンタンメン) are expanded into their constituent style vocabulary before embedding (transparent via concept_expansion in the echoed query) — expansion never adds filters, so shops serving the dish always stay eligible; spicy-implying concepts additionally give spice-verified shops a small rank boost (concept_boost — a soft rerank, still no filter). Richness/hours inferred from the query text likewise act as a soft rank boost (attr_boost; attr_matched is informational) — only explicit richness/hours params and spiciness intent filter strictly.
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  • A game kit IS an engine: its manifest carries asset slots AND GameRules — the genre as a document (movement, camera, world, objective, entities, pickups, ai, hud, audio). action=list → kits of the workspace. action=get {id} → manifest + rules (lib_… id). action=create {style?, name?, rules?, slots?} → new kit, rules default to the style then merge yours. action=patch {id, rules?, slots?, prompts?, name?} → partial update; rules merge section by section, a section set to null resets it. Slot refs inside rules must point at slot ids of the kit (unknown refs are dropped, the renderer falls back to procedural). Compose a NEW genre by writing rules: e.g. movement vehicle-boost + world arena-ring + objective knockout = sumo cars, no code.
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  • Search Partle's product catalog by name or description. CRITICAL SEARCH INSTRUCTION: Reason from the job to the product class first, then search with a descriptive product phrase (e.g. including substrate, material, or size class). DO NOT blindly search using the user's raw conversational words. Transform questions like 'what do I need to attach a mirror to a brick wall?' into a product phrase like 'heavy duty masonry wall anchor'. Two distinct modes: - **Default (no flags)** — fast keyword search. ~100ms. Acts like a normal "dumb" search box: matches the literal words you typed against product names and descriptions, with stemming. Good for queries where the user knows the product's likely name ("BC547", "Arduino Uno", "Bosch drill"). Returns noisy/wrong results on cross-language or attribute queries ("compost bin" matches Spanish "composta", not real composters). - **`super_search=True`** — slow, high-quality. ~1–2s. Run when the user describes what they want rather than naming it: cross-language ("Schraubenzieher Set" → real screwdriver sets even without German catalog entries), attribute-style ("small metal part with a flat head"), or any case where the default returns junk. Embeds the query with voyage-3-large, takes the cosine top-50 over the corpus (with an exact-name precision boost for part numbers), then a cross-encoder reranks them. The two modes are mutually exclusive in practice — pick one based on whether the user knows the product's name or is describing it. Use this when the user asks to find a specific product or browse products matching a query. Prefer over `search_stores` when the intent is product-led ("find a drill") rather than store-led. Use `get_product` afterwards if the user wants full details for one specific result. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search term. In default mode, treated as keywords (each word matched against product text). In `super_search=True`, treated as a natural-language description. min_price: Lower bound on price in EUR. Omit for no lower bound. Null-priced rows are NOT excluded by this filter — pass `has_price=True` if you need only priced listings. max_price: Upper bound on price in EUR. Omit for no upper bound. Tip — narrow by budget: `min_price=10, max_price=50, sort_by="price_asc", has_price=True`. Products without a listed price (a large fraction of the scraped catalog) sort last under either price ordering and are kept in results unless `has_price` filters them out. tags: Comma-separated tag filter (e.g. "electronics,bluetooth"). Tags are AND-ed together. store_id: Restrict results to a single store. Use the integer `id` from `search_stores` results. sort_by: One of `price_asc`, `price_desc`, `name_asc`, `newest`, `oldest`. Omit to use the default search-relevance ranking. has_price: When True, exclude products without a listed price (~most of the scraped catalog). Use this for competitive pricing or budget-bounded shopping. When False, return only null-priced listings (rarely useful). Omit to include both. semantic: Legacy flag. Pure vector ordering, ~250ms. Mostly superseded by `super_search=True` (which uses the same vector retrieval plus a cross-encoder rerank for materially better ordering at the cost of another ~700ms). Keep using it only if you specifically want vector retrieval *without* the rerank. super_search: **Enable for natural-language / "describe what I want" queries.** ~1–2s. Embeds the query with voyage-3-large, takes the cosine top-50 (with a precision boost for exact-name matches like part numbers / SKUs), then a cross-encoder reranks them. Use whenever the user is describing rather than naming — cross-language ("Schraubenzieher Set"), attribute-style ("small black metal bracket"), or any case where the default keyword path returns junk. Don't combine with cheap browse-style queries where the user typed an exact product name — keyword default is faster there. On `relevance_score` here: better than the bi-encoder cosine, but still not a "did I find what the user wanted" gauge. Behavior to expect: gibberish or fully-off-topic queries cap around 0.35; loosely-related catalogue clusters can score 0.7+ even when no item truly matches (a "ceramic vase" query in a catalog with no vases but many ceramic flowerpots will still score high). **Read the product names** before claiming a match. The score is most useful as a relative signal within one result set — a sharp drop between rank N and N+1 marks where the catalog stops being useful for this query. limit: Max results (1–100, default 20). Larger limits are slower and consume rate budget faster. offset: Skip this many results before returning. Use for pagination (offset += limit on each follow-up call). Returns: A list of products. Each includes `id`, `name`, `price`, `currency`, `url`, `description`, `store` (id/name/address), `tags`, `images`, a canonical `partle_url`, and `relevance_score` (cosine similarity 0–1 between the query and the product's embedding when a query was provided; `None` otherwise). **Always share `partle_url` with the user so they can view the listing.** Caveat on `relevance_score`: it is monotonic *within a single search result set* (useful for spotting a big drop-off between rank 3 and rank 4), but its absolute value is not well-calibrated across queries — most results land in 0.55–0.80 regardless of whether the catalog has truly relevant items. Don't infer "this is a great match" from a 0.75 score alone.
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  • Search Partle's product catalog by name or description. CRITICAL SEARCH INSTRUCTION: Reason from the job to the product class first, then search with a descriptive product phrase (e.g. including substrate, material, or size class). DO NOT blindly search using the user's raw conversational words. Transform questions like 'what do I need to attach a mirror to a brick wall?' into a product phrase like 'heavy duty masonry wall anchor'. Two distinct modes: - **Default (no flags)** — fast keyword search. ~100ms. Acts like a normal "dumb" search box: matches the literal words you typed against product names and descriptions, with stemming. Good for queries where the user knows the product's likely name ("BC547", "Arduino Uno", "Bosch drill"). Returns noisy/wrong results on cross-language or attribute queries ("compost bin" matches Spanish "composta", not real composters). - **`super_search=True`** — slow, high-quality. ~1–2s. Run when the user describes what they want rather than naming it: cross-language ("Schraubenzieher Set" → real screwdriver sets even without German catalog entries), attribute-style ("small metal part with a flat head"), or any case where the default returns junk. Embeds the query with voyage-3-large, takes the cosine top-50 over the corpus (with an exact-name precision boost for part numbers), then a cross-encoder reranks them. The two modes are mutually exclusive in practice — pick one based on whether the user knows the product's name or is describing it. Use this when the user asks to find a specific product or browse products matching a query. Prefer over `search_stores` when the intent is product-led ("find a drill") rather than store-led. Use `get_product` afterwards if the user wants full details for one specific result. Read-only. No authentication. Rate-limited to 100 requests/hour per IP. Args: query: Free-text search term. In default mode, treated as keywords (each word matched against product text). In `super_search=True`, treated as a natural-language description. min_price: Lower bound on price in EUR. Omit for no lower bound. Null-priced rows are NOT excluded by this filter — pass `has_price=True` if you need only priced listings. max_price: Upper bound on price in EUR. Omit for no upper bound. Tip — narrow by budget: `min_price=10, max_price=50, sort_by="price_asc", has_price=True`. Products without a listed price (a large fraction of the scraped catalog) sort last under either price ordering and are kept in results unless `has_price` filters them out. tags: Comma-separated tag filter (e.g. "electronics,bluetooth"). Tags are AND-ed together. store_id: Restrict results to a single store. Use the integer `id` from `search_stores` results. sort_by: One of `price_asc`, `price_desc`, `name_asc`, `newest`, `oldest`. Omit to use the default search-relevance ranking. has_price: When True, exclude products without a listed price (~most of the scraped catalog). Use this for competitive pricing or budget-bounded shopping. When False, return only null-priced listings (rarely useful). Omit to include both. semantic: Legacy flag. Pure vector ordering, ~250ms. Mostly superseded by `super_search=True` (which uses the same vector retrieval plus a cross-encoder rerank for materially better ordering at the cost of another ~700ms). Keep using it only if you specifically want vector retrieval *without* the rerank. super_search: **Enable for natural-language / "describe what I want" queries.** ~1–2s. Embeds the query with voyage-3-large, takes the cosine top-50 (with a precision boost for exact-name matches like part numbers / SKUs), then a cross-encoder reranks them. Use whenever the user is describing rather than naming — cross-language ("Schraubenzieher Set"), attribute-style ("small black metal bracket"), or any case where the default keyword path returns junk. Don't combine with cheap browse-style queries where the user typed an exact product name — keyword default is faster there. On `relevance_score` here: better than the bi-encoder cosine, but still not a "did I find what the user wanted" gauge. Behavior to expect: gibberish or fully-off-topic queries cap around 0.35; loosely-related catalogue clusters can score 0.7+ even when no item truly matches (a "ceramic vase" query in a catalog with no vases but many ceramic flowerpots will still score high). **Read the product names** before claiming a match. The score is most useful as a relative signal within one result set — a sharp drop between rank N and N+1 marks where the catalog stops being useful for this query. limit: Max results (1–100, default 20). Larger limits are slower and consume rate budget faster. offset: Skip this many results before returning. Use for pagination (offset += limit on each follow-up call). Returns: A list of products. Each includes `id`, `name`, `price`, `currency`, `url`, `description`, `store` (id/name/address), `tags`, `images`, a canonical `partle_url`, and `relevance_score` (cosine similarity 0–1 between the query and the product's embedding when a query was provided; `None` otherwise). **Always share `partle_url` with the user so they can view the listing.** Caveat on `relevance_score`: it is monotonic *within a single search result set* (useful for spotting a big drop-off between rank 3 and rank 4), but its absolute value is not well-calibrated across queries — most results land in 0.55–0.80 regardless of whether the catalog has truly relevant items. Don't infer "this is a great match" from a 0.75 score alone.
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  • Scan the ENS marketplace for alpha — names listed below their valuation. Returns ranked opportunities with a discount %, fair-value range, confidence rating, and comparable data. Candidates are selected by DESIRABILITY (real curated collections, short, accessibly priced above a floor that excludes 0.001-ETH floor-dumps), then each is precision-priced by the full Name Whisper valuation engine — the SAME engine behind get_valuation and the Value page — which is the sole judge of undervaluation. The returned fair-value range (estimatedValueEth), confidence and discountPct are the engine's own numbers, via the same cache-first path as get_valuation (with display-only signals disabled for speed), so they are authoritative and consistent with get_valuation. They are computed conservatively (the seller-wallet boost is off), so if anything they slightly UNDERSTATE fair value — report them as-is; do NOT inflate the fair value or upgrade the confidence. Use estimatedValueEth.mid as the fair-value anchor. Only opportunities the engine confirms are surfaced: a believable discount band (20%+, capped where valuations stop being reliable), MEDIUM+ confidence, and a REAL comparable-sale match (type/collection/word/entity/semantic — never a coarse same-length average). This means genuinely good, believable deals (typically 25–65% off) — not 99%-off junk. It will still surface a large discount when the engine confirms it with real comps; it just won't fabricate one. **Use this instead of search_ens_names + repeated get_valuation when the user asks for "best value", "best buy", "cheapest good name", "undervalued", "bargains", or any ranked-by-value query across multiple listings.** find_alpha does the search + engine valuation + ranking in a single call — you do NOT need to call get_valuation again on its results. If it returns fewer names than asked, the rest weren't genuine discounts vs the engine — say so rather than padding the list. Supports filters (minLength, maxLength, maxPriceEth, charType) so narrow queries like "4-letter names under 1 ETH, best value" are one call, not six. It has NO collection/category/club param. Do NOT use it for "floor price of the 999 club", "cheapest 10k-club names", or "floor of <collection>" — those name a specific collection, so use search_ens_names (which returns that collection's real listings sorted by price), or sweep if the user wants to buy the cheapest N. find_alpha is for value-ranked discovery across the market, not a named collection's floor.
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  • Given a Camelot key (e.g. "8A", "12B"), return the harmonically compatible keys for DJ mixing — the same key, the relative major/minor, and the adjacent +/-1 keys on the Camelot wheel. With `extended=true` also returns the +7/-7 energy-boost / energy-drop keys. Pure music theory — no catalog lookup and no quota cost. Pair with find_tracks_by_key to then pull actual tracks in each compatible key.
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  • Remember how the user feels about an author in their list digests. action=boost → never miss this author's posts (they will always be included, low-signal ones collapsed rather than dropped); action=hide → mute them; action=clear → forget the preference. Use when the user says things like 'don't let me miss marclou's updates' or 'stop showing me X'. Takes effect from the next digest and in preview_list_ranking immediately.
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  • Record the user's correction to an inferred interest, using a key from get_interests. Corrections are sticky — they survive every future recompute. Use `confirm` when they agree, `rename` for their own wording, `hide` to stop using an interest while keeping its history, `boost` when they want it weighted higher. `forget` is different and IRREVERSIBLE: it erases the underlying history so the topic cannot re-form — only use it when the user clearly asks to delete, and say what it means first.
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  • Search the Crossload catalogue of German Christian content (sermons, books, audio, images) and get matching items with title, author, licence, duration and a teaser. Use it whenever the question is what was preached, written or said about a topic, a bible passage, by an author, or within a length limit ('something good under 30 minutes'). Use 'uids' to narrow a set of items you already found; it returns the items, not the position of a passage inside them. Topic, author and series take names and are resolved server-side; if a name is not an exact match the search is NOT run and the answer says so — call crossload_browse to get the exact spelling. Bible references are given in German ('Epheser 2', 'Joh 3,16-18') and filtered by range, verses included. Note that 'query' is matched semantically, not literally: it always returns something, so a hit is not proof that the words appear in the text. Combine it with a filter when precision matters. Natural-language questions (German question words, '?', six or more words) trigger stronger semantic matching. A bible reference inside the query text triggers a server-side boost on matching content that may skew results — use the 'bibleRef' parameter for deliberate passage filtering instead. Set 'withExcerpts' when you need to quote WHERE something is said: every hit then carries an excerpt around the match, and matching becomes literal, so a hit does prove the words appear and no hit means they do not.
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  • The product's global full-text search (OpenSearch): cards, epics, wiki pages and projects, matching titles, descriptions, comments, checklist items and attachment filenames, relevance-ranked with a recency boost. Use this to find anything by content ("the card where we discussed the migration"); use search_cards only for title-substring lookups inside one project. Each result carries a type, an action-ready uid (cards feed get_card/update_card, epics get_epic, wiki_pages show_wiki_page, projects the project_id parameters), a deep-link url, and <mark>-highlighted snippets showing why it matched. Optionally scope with project_id or assigned_to_me. Paginate with page/per_page; total_count is the full match count.
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  • Select a shipping method for the cart. Must call get_shipping_rates first and provide idempotency_key. Reuse the same key for retries of the same cart and option; use a new key if either changes. PREFER THE CART CARD: if there is an interactive card for this cart, let the buyer pick shipping there instead of calling this tool yourself — it keeps the flow in one card instead of opening a new one per step. SKYFIRE TOKEN (optional): Pass a kya or kya-pay token in the skyfire-pay-id header to boost trust score. No token required — request proceeds normally if omitted. Checkout guide: https://trusteed.xyz/.well-known/agent-checkout-guide.json. CREDENTIAL: this store needs one. Call the `create_sandbox_key` tool first (it is in this tool list and needs no credential), then pass the key you get back as the `agent_key` argument — or as an `Authorization: Bearer` header if your client can set headers. Do not ask a person to log in: there is no human login for this store.
    ConnectorNo auth
  • Preview the complete order summary before payment. Advances the session to ready-for-payment state. PREFER THE CART CARD: if there is an interactive card for this cart, let it compute and show the summary instead of calling this tool yourself — it keeps the flow in one card. SKYFIRE TOKEN (optional): Pass a kya or kya-pay token in the skyfire-pay-id header to boost trust score. No token required — request proceeds normally if omitted. Checkout guide: https://trusteed.xyz/.well-known/agent-checkout-guide.json. CREDENTIAL: this store needs one. Call the `create_sandbox_key` tool first (it is in this tool list and needs no credential), then pass the key you get back as the `agent_key` argument — or as an `Authorization: Bearer` header if your client can set headers. Do not ask a person to log in: there is no human login for this store.
    ConnectorNo auth
  • Rally the ProductClank community to engage with a specific social post. Creates a boost campaign and spends the user's credits: 'replies' generates 10 AI reply drafts (200 credits); 'quote' generates 10 AI-drafted QUOTE POSTS (200 credits) — a repost WITH each member's own text, shown above the quoted post in their followers' feeds, the highest-reach action; 'likes' (30 likes) and 'repost' (10 reposts) cost 300. Supports Twitter/X, Instagram, TikTok, LinkedIn, Reddit, Farcaster, and YouTube (replies + likes on YouTube; reposts only on X and Farcaster; quote posts only on X) — the platform is auto-detected from the URL. product_id is OPTIONAL: link a product (from search_products or create_product) to tailor replies with the product name, or omit it for a tweet-first boost that uses generic amplification language. Confirm the action and its credit cost with the user before calling.
    ConnectorNo auth
  • Currently boosted/promoted Base tokens — Fetches the DexScreener boosts feed filtered to Base — up to 15 trending tokens with address, description, boost amount, and link. Great for discovery bots. Priced $0.01 per call over x402 on Base; send a prepaid x-credit-token header for unlimited calls, or get 1 free call/day per tool. No wallet or API key required.
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  • Start an ad campaign that sends players to the sandbox page. Send exactly one of bonusId or postUrls. A referral bonusId (from get_referral_program or get_sandbox_traffic) spends that $3 traffic bonus without a social post and without using the separate share allowance. Reuse the same bonusId and applicationId after an uncertain answer. Poll the returned run id; a timeout is not permission to spend another bonus. **The separate share traffic bonus: first help the developer post the game (get_sandbox_share has the post and links), then ask the developer to paste the links of their published posts and pass 1–3 of them (different public HTTPS links from any platform) — never invent one. Each accepted link adds one boost unit to the campaign, up to three. One campaign per game, three games per organization for its lifetime; earlier free runs without sharing do not count. No dollar amount.** Ask the developer first. Needs covers in the published revision and the Bridge SDK. Answers the PENDING run; poll get_sandbox_traffic for the returned run.id until it leaves PENDING — FAILED carries `failureReason`. Only a confirmed safe failure makes a referral bonus retryable; an uncertain DSP outcome keeps its reservation. A refusal carries the `reason` get_sandbox_traffic shows, or SHARE_REQUIRED / INVALID_POST_URL for missing or invalid links; PAYMENT_REQUIRED: paid traffic is in the cabinet. Repeatedly submitting already accepted links reuses the same grant without changing its budget or dates. Add the remaining links later (up to three total) to increase the same campaign budget and extend it to at least three days from submission, including after it has finished. New links wait while a launch/update is PENDING (RUN_IN_PROGRESS). A stale PENDING run is requeued with the same id and saved links.
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    Destructive
    API key