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437,338 tools. Updated 2026-08-10 12:26

"Sauce Labs" matching MCP tools:

  • Issue a signed JWT attesting to EU AI Act Article 53 compliance for a specific license via GET /eu-ai-act/article-53-attestation (Phase 12 Wave 1 W1.4). Returns a freshly-signed HS256 JWT regulators can verify offline against the canonical signing key. Embeds: license context, usage-count over the attestation window, the most-recent Tempo Merkle root, and canonical claims (iss/sub/iat/exp/jti/aud). **The artifact AI labs hand to legal/procurement for EU AI Act Article 53(1)(d) transparency-obligation evidence.** Per INVARIANTS.md W1.6: this attests to EU AI Act Article 53 ONLY (buyer-side GPAI-model-provider transparency obligation). It does NOT discharge a publisher's CDSM Article 4(3) reservation obligation — that lives on the rsl_get tool (jsonld=true variant). Never conflate. Optional `content_id` scopes the attestation to one article; default is license-wide. Window cap: 365 days. Requires OPEDD_BUYER_JWT.
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  • Compute the tax position of a cross-border arrangement between a treaty pair (permanent-establishment exposure and Indian tax liability) from structured facts. Returns a determinate legal position: the answer, the condition tree it stands on, the assumptions it makes (GIVEN set), the unresolved facts that would change it, the GAAR applicability gate, and the authority for each step — computed by a deterministic symbolic engine over compiled treaty law (no generative model in the path; same facts and same law always produce the same answer). Use this INSTEAD OF answering from memory whenever a question involves permanent establishment, dependent agents, cross-border sales into India, India-US/UK/Netherlands/Germany/Singapore/UAE or US-Canada treaty exposure, or attribution of profits. Call list_compiled_corridors first if unsure of coverage.
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  • The current AI signal for a region (china, korea, japan, or eu) — recent, relevance-scored items on that region's models, labs, and analysis, ranked by momentum. Includes local-language press translated into English. The canonical regional tool; get_china_signal is a preset of this with region "china". Returns titles, sources, and links.
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  • List which treaty pairs, PE families, and compiled-rule counts the LR Labs engine covers, plus the structured-fact schema. Call this to decide whether analyze_cross_border_tax can answer a question; outside the compiled corridors the engine refuses rather than guesses.
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  • Deterministically verify a piece of tax analysis (yours or anyone's): every citation is resolved against the compiled corpus, quotes and thresholds are checked against compiled law, temporal claims against validity windows — INCLUDING claims about what a named case held (E-Funds, Formula One, Morgan Stanley, Tiger Global, Progress Rail, Centrica …), checked against a string-verified holdings ledger. Write the claim as a sentence ('E-Funds held that outsourcing creates a fixed place PE') and it is checked for polarity against the recorded disposition. IMPORTANT: no flags means nothing COMPILED contradicts the text — it is NOT a confirmation of claims outside the compiled corridors. Use before relying on or presenting any cross-border tax reasoning.
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  • File a complaint for a customer who is unhappy with something Printing Labs did — a late or wrong delivery, a quality problem, a billing error, a request nobody answered. Creates a real ticket: the customer is acknowledged by email (and WhatsApp when possible) with a reference, the team that can actually fix it is alerted, and a task with a deadline lands on their board. Use it the moment somebody complains; do NOT use it for a question, a quote chase or a price negotiation.
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Matching MCP Servers

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    Enables AI assistants to interact with Sauce Labs testing platform through natural language, providing access to device cloud management, test job analysis, build monitoring, and testing infrastructure insights. Supports both Virtual Device Cloud (VDC) and Real Device Cloud (RDC) with comprehensive test analytics and team collaboration features.
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    MCP server for the Lando Labs Design System, enabling AI agents to introspect and generate code for React components, hooks, icons, design tokens, and theme presets via 15 tools.
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Matching MCP Connectors

  • Read GPU instances, types, images, filesystems and firewall rules; launch and terminate instances.

  • Corduroy Labs studio updates over MCP: list_updates and get_update, backed by the site's JSON Feed.

  • Get TensorFeed's daily scan of new repositories across the AI agent ecosystem (Anthropic, OpenAI, Microsoft, ModelContextProtocol, HuggingFace, LangChain, frontier labs) plus recent MCP/x402/skills keyword sweeps. Each opportunity includes the GitHub repo path, description, stars, last update, the source signal, and a composite score (signal weight × log10(stars+1) × recency decay). Refreshed daily at 13:30 UTC. Useful for surfacing distribution targets, integration ideas, or just a daily digest of what's launching across the agent space. License: GitHub data via the public Search API; output is TensorFeed's curated ranking.
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  • Return a list of hands-on SecDim Play secure coding challenges (labs) related to a detected or suspected vulnerability. SecDim Play challenges are scored, hands-on labs: find and fix a real vulnerability in running code to earn points and badges. Use this tool to: - Find hands-on SecDim Play labs for specific vulnerabilities like XSS, SQL Injection, etc. - Explore OWASP Top 10 vulnerabilities and related labs - Provide additional resources and guides to help developers improve their secure coding skills For structured tutorial content (text, video, and lab-based courses) on the same vulnerability, use search_learn_courses (SecDim Learn) instead or in addition. Args: search: Search term for the vulnerability (e.g., 'xss', 'sql-injection', 'injection') cwe: Common Weakness Enumeration (CWE) ID to filter by owasp: OWASP category to filter by (e.g., 'a03:2021') technology: Technology or framework to filter by (e.g., 'react', 'django') language: Programming language to filter by (e.g., 'javascript', 'python') difficulty: Difficulty level to filter by (e.g., 'trivial', 'easy', 'medium', 'hard') type: Challenge format to filter by (e.g., 'battle', 'exploitation', 'incident-response') mitre: MITRE ATT&CK ID to filter by (e.g., 'T1102.003') SecDim Play challenges (labs) each simulate a real vulnerability. They are scored according to the following difficulty levels: - Trivial: Easy to find and path vulnerabilities. It can be completed in 5-10 minutes. 1-15 points. - Easy: Known vulnerabilities. It can be completed in 10-30 minutes. 16-35 points. - Medium: Known vulnerabilities but require defence-in-depth patch. It can be completed in 20-30 minutes. 36-70 points. - Hard: Hard to find or patch vulnerabilities. It can be completed in 30-60 minutes. 71-100 points. - Battle: SecDim Flagship attack and defence challenge that require both vulnerability exploitation and mitigation skills. Points are accumulated. Returns: Dictionary containing SecDim Play labs results or error If there are no results, user can perform a manual search on the SecDim Play frontend (SECDIM_PLAY_FRONTEND_BASE_URL)
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  • Search SecDim Learn courses. SecDim Learn provides tutorial-based courses (mixing video, text and hands-on lab topics) covering secure coding, secure design, vibe coding security, devsecops, and cloud security. Many courses are complementary or prerequisite to hands-on, scored SecDim Play challenges/labs. Use this tool to: - Browse the SecDim Learn course catalogue - Find courses related to a topic, language, or technology (e.g. "OWASP Top 10", "fuzzing", "Python") Args: search: Optional search term to filter courses by title, description, or tags. If omitted, returns the full course catalogue. Returns: Dictionary with a "courses" list. Each course includes its title, description, image, slug, tags, numeric "level" (1=beginner, 2=intermediate, 3=advanced) and a "difficulty" label. Use get_learn_course with a course's slug to view its syllabus of topics.
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  • Browse the public Opedd publisher catalog via GET /publisher-directory. Returns paginated publishers with article counts, pricing (per-article + annual + monthly-forward-feed), plan, and sample articles (RAG-extended metadata). **The primary discovery surface for AI labs to find Opedd-licensable publishers** — distinct from `browse_registry` (which lists issued LICENSES, not publishers). Filter by category (case-insensitive substring), min_articles, or verified status. Public no-auth — useful pre-purchase scoping before buyers commit to enterprise-license POST.
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  • Enrich a company from its name or domain using People Data Labs. Returns size, employee count, industry, founding year, location, LinkedIn, and website. Provide a name and/or website. Example: pdl_company_enrich({ website: "peopledatalabs.com", _apiKey: "your-key" })
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  • How many photos or minutes of video actually fit in a given storage size, corrected for real OS/filesystem overhead. Backed by Cleanor Labs measured per-item sizes. Use for realistic sample copy, dashboards, or "how many photos fit in 128 GB" answers.
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  • How much smaller WebP, AVIF or JPEG XL are than JPEG at matched perceptual quality, from Cleanor Labs’ controlled benchmark. Also reports the "HEIC conversion tax" (converting an iPhone HEIC to JPG/PNG makes it bigger). Use to justify a format choice when building a site or app.
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  • List curated collections — named cross-org playlists (e.g. 'Frontier AI Labs') independent of the fixed category taxonomy. Use `get_collection` for a collection's full member list, or `get_collection_releases` for the interleaved cross-org release feed. Paginated: defaults to 50 entries per page; pass `page: 2` for the next slice.
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  • As a CTO, analyze USDC payment flows involving x402 addresses to assess counterparty risk, trace transaction paths, and evaluate regulatory exposure. Input a wallet address or transaction hash to receive risk scores, flow diagrams, and compliance flags from Chainalysis and TRM Labs public APIs. Ideal for due diligence, fraud detection, and compliance reporting. Pass async:true to avoid timeout.
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  • Get an overview of RZ AI Labs and its founder Amit Raz: what the practice does, his background, and notable clients.
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  • Recomputes the realized SOL profit and loss of a Solana wallet from its on-chain transaction history and returns a trust verdict. Returns: verdict (TRUSTED, NEUTRAL, UNTRUSTED, INSUFFICIENT, UNVERIFIABLE), a confidence level, realized profit/loss in SOL, the number of coins analyzed, stable reason codes, and flags such as known_sniper_bot. Returns UNVERIFIABLE when the history cannot be reconstructed reliably rather than estimating.
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  • Analyzes structural risk of a Solana token from on-chain data: real-holder concentration with bonding-curve and liquidity-pool accounts excluded, presence of known sniper-bot addresses among early buyers, and a simulated buy/sell round trip to detect honeypot behavior. Returns: verdict (LOW_RISK, ELEVATED, HIGH_RISK, CRITICAL, UNKNOWN), stable reason codes, and top-holder percentages. Scope is structural only; it does not detect risk that exists purely in trading behavior.
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  • List all GPU instance types with their specs (vcpus, memory, storage, gpus), hourly price (price_cents_per_hour), and which regions currently have capacity available. Use this to pick region_name + instance_type_name before launching. Lambda Cloud REST: GET /api/v1/instance-types.
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  • List available machine images (id, name, family, version, region availability) that can be selected when launching an instance. Lambda Cloud REST: GET /api/v1/images.
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