"Searching for a Chrome MCP" matching MCP tools:

• getTiktokPostsBySoundA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get Tiktok posts that use a specific sound/music with three response modes. Required: soundId (numeric ID from searchTiktokSounds). FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. Returns by default: id, description, username, createdAtDate. NOT for keyword/phrase search - use getTiktokPostsByKeywords. NOT for hashtags - use getTiktokPostsByHashtags. Date filters: OMIT startDate/endDate by default. ONLY pass if user explicitly requests a date range (YYYY-MM-DD). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Core (id, description, userId, username, nickname, createdAtDate), Engagement (likeCount, commentCount, playCount, forwardCount, collectCount, downloadCount), Media (videoThumbnail, videoUrl, duration, postType), Content (hashtags, transcriptsJson). This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• code_searchA

  searchcodeA

  Fast code search across any public git repo. Returns file paths, line numbers, and code snippets with context. Supports regex, boolean queries, fuzzy matching, and structural filters (declarations, usages, strings, comments). Dependency/build directories excluded by default. Searching for leaked secrets? Call code_analyze instead and read security_summary — it runs a real secret and credential scanner over the whole repository, where searching for key prefixes like "sk-", "ghp_" or "AIza" only matches the spellings you happened to think of.

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• batch_searchA

  Pinch Grocery Prices (Australia)A

  ADVANCED / manual selection. Do NOT use this to build a shopping list: for any list of 2 or more items, call build_basket (one call, server-side, cheapest pick per item). Use batch_search only when you need the RAW candidate products for items you intend to select MANUALLY (for example, to override build_basket's pick on one item). It searches multiple items in parallel and returns raw candidate products per query (up to candidates_per_retailer per retailer) with NO filtering or scoring applied. WORKFLOW: 1. Call batch_search with your shopping list items 2. Review the candidates for each item. For each query, pick the single best product_id per retailer that actually matches what the user wants (correct product type, reasonable size, not a different product). If none of the candidates are a good match for a query, call batch_search again with a refined query (e.g., more specific terms, different wording) 3. Once you have confirmed product_id selections for all items, call save_basket with your picks TIPS: - Set product_type to help narrow results to the right category (e.g., fresh_produce for herbs and vegetables) - Each candidate includes a product_categories field (JSON array of retailer category labels like 'Yoghurt', 'Herbs & Spices', 'Sausages'). Use this to distinguish the actual product type when names are ambiguous (e.g., 'oregano' the herb vs 'oregano sausages') - Prefer products where the name clearly matches the query intent - For staples (milk, eggs, bread), the cheapest option is usually the store brand - If results look wrong (e.g., searching 'rosemary' returns snacks instead of herbs), retry with a more specific query like 'fresh rosemary' - Each item includes a 'cheapest' hint with one entry per retailer. This is the cheapest candidate whose product_categories match the inferred subcategory for the query, NOT necessarily the lowest unit_price overall: a clearly off-category cheaper item (e.g., a snack bar under 'peanut butter') is deliberately excluded. When no category signal is available it falls back to the raw lowest unit_price. Treat the hint as advisory and confirm against the full candidate list and the user's intended format. AMBIGUITY: When candidates for a query span clearly different product formats (e.g., microwave rice pouches vs dry rice bags, single-serve vs family/bulk sizes, flavoured vs plain variants, fresh vs frozen), ASK the user which format they prefer before selecting a product. Compare the size field across candidates to detect this: a spread from 125g to 5kg signals format ambiguity. Do not silently pick the cheapest option when it may be the wrong format (e.g., a 250g microwave pouch when the user likely wants a 1kg bag).

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• batch_searchA

  PinchA

  ADVANCED / manual selection. Do NOT use this to build a shopping list: for any list of 2 or more items, call build_basket (one call, server-side, cheapest pick per item). Use batch_search only when you need the RAW candidate products for items you intend to select MANUALLY (for example, to override build_basket's pick on one item). It searches multiple items in parallel and returns raw candidate products per query (up to candidates_per_retailer per retailer) with NO filtering or scoring applied. WORKFLOW: 1. Call batch_search with your shopping list items 2. Review the candidates for each item. For each query, pick the single best product_id per retailer that actually matches what the user wants (correct product type, reasonable size, not a different product). If none of the candidates are a good match for a query, call batch_search again with a refined query (e.g., more specific terms, different wording) 3. Once you have confirmed product_id selections for all items, call save_basket with your picks TIPS: - Set product_type to help narrow results to the right category (e.g., fresh_produce for herbs and vegetables) - Each candidate includes a product_categories field (JSON array of retailer category labels like 'Yoghurt', 'Herbs & Spices', 'Sausages'). Use this to distinguish the actual product type when names are ambiguous (e.g., 'oregano' the herb vs 'oregano sausages') - Prefer products where the name clearly matches the query intent - For staples (milk, eggs, bread), the cheapest option is usually the store brand - If results look wrong (e.g., searching 'rosemary' returns snacks instead of herbs), retry with a more specific query like 'fresh rosemary' - Each item includes a 'cheapest' hint with one entry per retailer. This is the cheapest candidate whose product_categories match the inferred subcategory for the query, NOT necessarily the lowest unit_price overall: a clearly off-category cheaper item (e.g., a snack bar under 'peanut butter') is deliberately excluded. When no category signal is available it falls back to the raw lowest unit_price. Treat the hint as advisory and confirm against the full candidate list and the user's intended format. AMBIGUITY: When candidates for a query span clearly different product formats (e.g., microwave rice pouches vs dry rice bags, single-serve vs family/bulk sizes, flavoured vs plain variants, fresh vs frozen), ASK the user which format they prefer before selecting a product. Compare the size field across candidates to detect this: a spread from 125g to 5kg signals format ambiguity. Do not silently pick the cheapest option when it may be the wrong format (e.g., a 250g microwave pouch when the user likely wants a 1kg bag).

  ConnectorNo auth

• list_supported_countriesA

  eSIM FinderA

  List every destination (country or region) the eSIMfinder catalog covers. Use this BEFORE calling search_esims when (a) the user asks "where can I use this?" / "kam môžem ísť?" / "what countries do you support?", (b) you are unsure whether a destination is in the catalog (especially for less common names — Curaçao, Vatican City, North Macedonia), or (c) you want to suggest alternatives after a search_esims call returned no results. Returns ~190 entries: Travel Pass (annual subscription covering 130+ countries) at the top, then countries and regional groupings (Europe, Africa, Asia Pacific, Middle East, etc.) in alphabetical order. The names returned here are the exact strings to pass as the `country` parameter to search_esims. No input parameters required. The response also lists which destinations offer the Premium Unlimited (PUL) high-data tier (Fixed and Unlimited Essential are available on almost every destination), so you can answer reverse questions like "which destinations have Premium Unlimited?" without searching each destination one by one. If the user wants to know exactly which countries are bundled inside a region (e.g. "what is in Europe Lite?"), use `get_esim_finder_info` with `topic="regions"` instead — that returns full per-bundle country lists.

  ConnectorOAuth

• contacts_searchA

  mcpA

  Lusha: looks up 1-25 known business contacts by Lusha ID, name+company, LinkedIn URL, or email. Use when: fetching contact details for known specific contacts (single or batch up to 25). Do not use when: searching for contacts by criteria (prospecting_contact_search covers that case). Optional 'enrich' bundles reveal data; pass false for preview-only, then prospecting_contact_enrich with each result's `canReveal[].field`. Returns: Lusha contact profiles with available business data. Credits: consumes 1 Lusha credit per successful lookup; adds reveal credits when enrich is true (default). Rate limits apply per Lusha plan (per-minute/hourly/daily); exceeding returns HTTP 429. The account_usage tool returns current limits and remaining headroom.

  ConnectorOAuth

• getTwitterPostsByKeywordsA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Search posts by keywords. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for fresh data. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: sentiment analysis, trend detection, content analysis across thousands of posts. Returns by default: id, text, authorUsername, createdAtDate. First searches database, then external API if data is stale or missing. NOT for URL lookups - use getTwitterPostsByIds. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). SORT: sortBy="relevance" (default, best matches first) or sortBy="latest" (newest first). Filters: language, authorId/authorUsername, countryCode (2-letter ISO code, feature-gated - only accounts with the country filter enabled; filters by the tagged place country in both database and on-demand results). Date filters: OMIT startDate/endDate by default. ONLY pass if user explicitly requests specific date range (YYYY-MM-DD format). Use filterOutRetweets=true to exclude retweets. IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, text, authorId, authorUsername, createdAt), Engagement (retweetCount, replyCount, quoteCount, impressionCount, bookmarkCount), Metadata (lang, source, suspended, deleted), Relations (conversationId, quotedTweetId, retweetedTweetId, replyToTweetId, replyToUserId, replyToUsername), Content (hashtags, mentions, mediaUrls), Location (placeName, placeCountry, placeCountryCode, placeBoundingBoxCoordinates, placeCentroid). This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• getTwitterPostRetweetsA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Get retweets of specific post. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Database-only. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. Database-only search for historical retweet data. Date filter: OMIT startDate by default. ONLY pass if user explicitly requests filtering from specific date (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Use to analyze post amplification patterns. NOT for quotes - use getTwitterPostQuotes. Optional fields parameter for performance: ["id", "authorUsername", "createdAt"]. This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• getTiktokPostsByKeywordsA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Search Tiktok posts by keywords with three response modes. Searches post descriptions and video transcripts. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: trend analysis, hashtag tracking, viral content discovery across Tiktok. Returns by default: id, description, username, createdAtDate. NOT for URL lookups or post ID lookups - use getTiktokPostsByIds instead. QUERY SYNTAX: Plain keywords (bitcoin, climate change), quoted phrases ("deep learning"), boolean expressions (AI AND crypto, bitcoin OR ethereum, politics NOT sports), or parenthesized groups ((startup OR entrepreneur) NOT "venture capital"). AND/OR/NOT must have a term on both sides. @handles like @karpathy are supported. Field operators (from:, lang:) are stripped. Forward slashes are treated as spaces (24/7 becomes 24 7). Date filters: OMIT startDate/endDate parameters by default to retrieve all posts. ONLY pass these if user explicitly requests specific date range (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, description, userId, username, nickname, createdAtDate), Engagement (likeCount, commentCount, playCount, forwardCount, collectCount, downloadCount), Media (videoThumbnail, videoUrl, duration, postType), Content (hashtags, transcriptsJson). This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• getTiktokPostsByHashtagsA

  Social Media Search API — Twitter, Instagram, Reddit, TikTok (XPOZ)A

  Search Tiktok posts by hashtags with three response modes. Searches the hashtags column directly (NOT post descriptions). Pass hashtags as an array of bare alphanumeric strings - do NOT include leading "#". OR semantics: matches posts containing ANY of the listed hashtags. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. CSV (responseType="csv"): Async single CSV download, returns operationId, poll for S3 link. CODE EXECUTION: For csv mode, download CSV and use code execution to analyze full dataset. Ideal for: hashtag trend analysis, viral hashtag tracking, cross-hashtag content discovery. Returns by default: id, description, username, createdAtDate. NOT for keyword/phrase search in descriptions - use getTiktokPostsByKeywords instead. NOT for URL or post ID lookups - use getTiktokPostsByIds instead. Date filters: OMIT startDate/endDate parameters by default to retrieve all posts. ONLY pass these if user explicitly requests specific date range (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. FIELDS parameter (optional): Specify to get additional/different fields. Available: Core (id, description, userId, username, nickname, createdAtDate), Engagement (likeCount, commentCount, playCount, forwardCount, collectCount, downloadCount), Media (videoThumbnail, videoUrl, duration, postType), Content (hashtags, transcriptsJson). This is a safe, read-only tool for analyzing searchable information.

  ConnectorOAuth

• search_hybridA

  Tripitaka MCP (hosted)A

  Hybrid search — combines keyword + semantic search via RRF. Uses Reciprocal Rank Fusion (RRF) to merge exact-word results with meaning-based results. **This is the recommended tool for "discourses about X" / concept queries**, because the semantic side catches suttas that discuss a concept using different vocabulary (e.g. some mindfulness-of-breathing suttas use `assasati/passasati/dīghaṁ` instead of `ānāpānassati`). ⚠️ **Send the question and nothing else. Do not pad the query.** The whole string becomes one vector, so every word you add moves it. Appending your own candidate terms — synonyms, Pāli equivalents, a keyword list — searches for the blend, not for the question. Measured on `Buddha flies in the sky`: asking it plainly put the right passage at **rank 1** (3 relevant suttas). Appending three guessed Pāli terms (`buddha, agga, sagga`) pushed it down to **rank 5** and left only 1 — because `agga` (supreme) and `sagga` (heaven) drag the vector toward their own meanings. Have candidate terms worth searching? Give them their own `search_by_keyword` call and merge the two result lists. One tool asks what a passage means, the other asks where a word occurs; combined into a single string they cancel out. Rewriting the question to sound more canonical does not help either — the same query phrased as `rose into the air and flew like a bird` scored **zero** relevant hits. 💡 **Hints for the AI client:** - English queries usually work best (e.g. `mindfulness of breathing`) because the embedding model is multilingual but EN-primary. - Thai stop-word handling is weak. If a Thai query underperforms, the AI client should translate to Pāli/English first (see server instructions). - The default `limit=5` is often too small for a topic survey — use `limit=15-20` (max 20) for good coverage. - `language` only chooses what comes back; it does not change what matches or how results are ranked. - Looking for a concrete thing rather than a concept (an animal, an object, a place)? `search_by_keyword` over `language="english"` is often better. The translations are segment-aligned to the Pāli, so one search for `turtle` finds every turtle passage whatever the Pāli underneath says (`kacchapa`, `kumma`, `maṇḍūkakacchapa`), and each hit still carries its segment id. - Ranking is by similarity, NOT canonical importance — locus classicus suttas (e.g. MN118, DN22) may rank below smaller suttas that happen to use the exact vocabulary. Treat results as a starting point, then call `get_sutta` for the canonical references.

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• list_docsA

  UploadkitA

  Enumerate every available UploadKit docs page with title, description, URL, and path. When to use: to discover what documentation exists before targeted searching, or to orient yourself around the shape of the docs site. Prefer search_docs when you already have a concrete question. Returns: JSON { count, generatedAt, pages: [{ path, url, title, description }] }. Pages are sorted alphabetically by path. Read-only, static at bundle time, idempotent.

  ConnectorNo auth

• analyze_responsesA

  IA-QA — 152 QA & Dev Tools for AI AgentsB

  Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pairwise cosine agreement, the most-representative output, and the outlier. With a `reference` (ground truth): also ranks every output by closeness (token cosine + ROUGE-L composite) and names the closest. No LLM, no key, runs locally. The reference mode is gate-able in CI; the consensus/outlier mode is a signal (no reference to fail against). You bring the outputs (2+). For a 2-way head-to-head with structural JSON diff use compare_responses instead.

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• hallucination_checkA

  IA-QA — 152 QA & Dev Tools for AI AgentsB

  Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. Each answer sentence is aligned to its best-matching source sentence, so a number only counts as support when it sits on the SAME statement ("founded in 1998" is not grounded by "sold 1998 units"), and a negation or antonym flip against that sentence returns verdict "contradicted" — the corrupted-fact hallucination that reuses source vocabulary. Limitations: still lexical — it cannot follow a paraphrase, a synonym, or multi-sentence reasoning, so a "well_grounded" verdict means "nothing lexical found", never "verified true". For entailment use run_semantic_tests (NLI/embedding) or a calibrated judge.

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• searchProductsA

  dm-drogerie-marktA

  Search for products available in the German dm-drogerie market (online and local stores). USE WHEN: searching dm-drogerie products by name, category, ingredient, property, or any natural language query (any language supported). Often answers questions about ingredients and properties directly. Covers: dm-drogerie markt brands, make-up, skincare, perfume, hair, health, nutrition, baby & child, household, home & living, photo, and pets. OUTPUT: Returns a maximum of 15 products. GTIN, DAN, brand, title, details, category, price, appLink (direct product URL), description, highlights/USPs, and extensive attributes including: - Dietary/Allergen: vegan, vegetarian, bio, glutenFree, lactoseFree, sugarFree, nutFree, soyFree - Cosmetic Ingredients: fragranceFree, alcoholFree, parabenFree, sulfateFree, preservativeFree, dyeFree, oilFree, siliconeFree, naturalCosmetics - Product Properties: waterproof, new, limitedEdition, sellout, onlineOnly, exclusiveDm, dmBrand, purchasable NOT FOR: nutritional information (calories, protein, carbs, fats), complete allergen lists, full ingredient details. For these, use 'getProductDetails' tool with the GTINs or DANs. LIMITATIONS: Only make claims based on EXPLICITLY stated product highlights/descriptions. Do NOT extrapolate or assume properties not mentioned in the results.

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• list_domainsA

  TunnelMind Data APIB

  Returns a paginated list of domains from the tracker database. Results are ordered alphabetically by domain name and support cursor-based pagination for full traversal. Filtering by category and minimum score allows targeted data extraction. Use this tool when: - You want to enumerate all known ad-tech or analytics domains above a risk threshold. - You need a dataset of tracker domains for offline analysis. - You are paginating through a category to build a block list. Do NOT use this tool when: - You need data for a specific domain — use `get_domain` instead. - You are searching by keyword — use `search` instead. - You want domains belonging to a specific company — use `get_entity` instead. Inputs: - `category` (query, optional): Filter by surveillance category. One of: `ad_tech`, `analytics`, `social`, `fingerprinting`, `content`, `cdn`, `other`. - `min_score` (query, optional): Integer 0-100. Exclude domains scoring below this value. - `limit` (query, optional): Number of results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from the previous response's `next_cursor` field. Returns: - Array of domain list items (domain, category, score, prevalence, entity summary). - `meta.has_more`: true if more pages exist. - `meta.next_cursor`: pass as `cursor` to get the next page. - `meta.count`: number of results in this page. Cost: - Free tier: up to 20 results/page. Anonymous (no key): 10/min, 100/day. Free key: 50/day. Pro/enterprise: up to 100 results/page, included in plan. Latency: - Typical: <200ms, p99: <500ms.

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• list_entitiesA

  TunnelMind Data APIB

  Returns a paginated list of corporate entities in the TunnelMind surveillance database. Includes data categories, estimated data value, and industry classification. Useful for enumerating the surveillance ecosystem by sector. Use this tool when: - You want to enumerate all entities in a specific industry (e.g., all ad-tech companies). - You need a dataset of surveillance entities for analysis or reporting. - You are building a comprehensive surveillance landscape map. Do NOT use this tool when: - You need the full profile of a specific entity — use `get_entity` instead. - You are searching by entity name — use `search` instead. - You need domain-level data — use `list_domains` instead. Inputs: - `industry` (query, optional): Filter by industry classification. Examples: `ad_tech`, `analytics`, `data_broker`, `social`, `crm`. - `limit` (query, optional): Results per page. Max 100 (paid), 20 (free). Default 50. - `cursor` (query, optional): Pagination cursor from previous response's `next_cursor`. Returns: - Array of entity list items (slug, name, parent_company, industry, data_categories, data_cost_usd). - `meta.has_more` and `meta.next_cursor` for pagination. Cost: - Free tier: up to 20 results/page. Anonymous (no key): 10/min, 100/day. Free key: 50/day. Pro/enterprise: up to 100 results/page, included in plan. Latency: - Typical: <150ms, p99: <400ms.

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• get_companyA

  Dice MCPA

  Retrieve the name and description of the company behind a job, by job ID. Use this tool when users want more detail about a company than search_jobs/get_job_details return (e.g. after finding a job and wanting to know more about the company that posted it). DO NOT use for: searching/discovering companies or jobs by keyword (use search_jobs instead). LLM USAGE INSTRUCTIONS: - The typical flow is: search_jobs to find jobs -> user picks one -> offer to look up the company -> if the user agrees, call this tool with that same job_id (the same guid used for get_job_details; no separate lookup step is needed first). - Do not call this automatically right after get_job_details; ask the user first unless they already explicitly asked for company details. - Not every company has published an employer branding profile, and some profiles are marked not visible by the company. When that happens this tool does NOT raise — it returns a Company with `message` set and empty name/desc. Relay that message to the user (e.g. "Looks like that company hasn't published a public profile.") rather than treating it as an error. Args: job_id: The unique identifier of the job whose company to look up (required). This must be the job's `guid` field from a search_jobs result, NOT its `id` field — `id` is a different, internal identifier that this tool does not accept. Returns: Company: Contains: - name: The company name (empty if message is set) - desc: The company description (empty if message is set) - message: Set instead of name/desc when there is no visible company profile to show; None when name/desc are populated Raises: Exception: If the job is not found, an API call itself fails (network error, non-200 response, GraphQL errors), or input validation errors occur

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• search_locationsA

  whentoflyA

  Resolve a city or airport name or code before searching flights. Returns typed values such as city:SHA to search every catalog airport in Shanghai or airport:SHA for Hongqiao only. Pass the selected value unchanged to search_flights. Ask the traveler when multiple results are plausible.

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• budget_verifyA

  Focxle: AI governance for B2B deals between agentsA

  Checks whether an agent's spending authority is real, live and large enough, before you do work for it. Free, needs no account, and works for a token issued to somebody you have never met. Returns the remaining budget, the scopes, and whether anything above it in the chain has been revoked. Use this instead of trusting a claim: the answer is signed, so you can keep it.

  ConnectorNo auth

• create_agentA

  WebRun Browser AutomationA

  Create a scheduled agent — a recurring or one-time automation that runs on a timer in a real Chrome browser. Two modes. WORKFLOW MODE: pass workflowId plus a schedule — the platform copies everything else (prompt, starting URL, output contract, model, proxy, policy, files) from the workflow and bakes your variables into the prompt; results and memory stay centralised on the workflow. STANDALONE MODE: omit workflowId and provide name, prompt (a Goal / Ground rules / Stages / Output browsing runbook — see the prompt field description), and a schedule; the connection's environment is used automatically. Ask the user only what it should do and when it should run, in plain language — pick sensible defaults for everything else and state them; never ask about environments or technical settings. Results are delivered to your connected chat and appear in session history.

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