Playwright MCP Server
mcp.so:altssdata/aguasto-research
Score: 90/100
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How to earn +10 points
Tools · 2
Find published Aguasto page titles, descriptions and canonical references. Follow the page for full sourced evidence.
Retrieve a published page's description, date, canonical URL and Markdown link. Not the full article text.
Resources · 90
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https://www.aguasto.com/developers/versioning
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https://www.aguasto.com/sandbox
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https://www.aguasto.com/
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https://www.aguasto.com/infrastructure
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https://www.aguasto.com/gpu-cloud
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https://www.aguasto.com/ai-inference
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https://www.aguasto.com/development
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https://www.aguasto.com/company
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https://www.aguasto.com/contact
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https://www.aguasto.com/privacy
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https://www.aguasto.com/terms
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https://www.aguasto.com/topics
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https://www.aguasto.com/topics/gpu-systems
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https://www.aguasto.com/topics/model-serving
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https://www.aguasto.com/topics/modular-infrastructure
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https://www.aguasto.com/topics/compute-economics
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https://www.aguasto.com/glossary
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https://www.aguasto.com/methodologies
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https://www.aguasto.com/developers
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https://www.aguasto.com/resources
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https://www.aguasto.com/gpus
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https://www.aguasto.com/models
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https://www.aguasto.com/inference-stack
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https://www.aguasto.com/learn
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https://www.aguasto.com/insights
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https://www.aguasto.com/research
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https://www.aguasto.com/research/cases
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https://www.aguasto.com/infrastructure/why-modular
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https://www.aguasto.com/infrastructure/cooling-resource-efficiency
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https://www.aguasto.com/partners
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https://www.aguasto.com/gpus/compare
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https://www.aguasto.com/models/compare
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https://www.aguasto.com/tools
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https://www.aguasto.com/research/hyperscale-development-friction
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https://www.aguasto.com/gpus/h100
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https://www.aguasto.com/gpus/h200
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https://www.aguasto.com/gpus/b200
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https://www.aguasto.com/gpus/b300
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https://www.aguasto.com/gpus/gb200-nvl72
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https://www.aguasto.com/gpus/gb300-nvl72
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https://www.aguasto.com/gpus/vera-rubin-nvl72
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https://www.aguasto.com/gpus/mi300x
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https://www.aguasto.com/gpus/mi325x
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https://www.aguasto.com/gpus/mi355x
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https://www.aguasto.com/gpus/mi455x
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https://www.aguasto.com/gpus/helios
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https://www.aguasto.com/models/glm-4-5
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https://www.aguasto.com/models/qwen3-235b-a22b
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https://www.aguasto.com/models/deepseek-r1
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https://www.aguasto.com/models/llama-3-3-70b-instruct
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https://www.aguasto.com/models/gemma-3-27b-it
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https://www.aguasto.com/models/gpt-oss-120b
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https://www.aguasto.com/models/kimi-k2-instruct
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https://www.aguasto.com/models/glm-5-3
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https://www.aguasto.com/models/qwen3-8-27b
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https://www.aguasto.com/models/deepseek-v4-1-flash
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https://www.aguasto.com/models/llama-4-scout-17b-16e-instruct
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https://www.aguasto.com/models/gemma-4-31b-it
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https://www.aguasto.com/models/kimi-k3
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https://www.aguasto.com/inference-stack/vllm
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https://www.aguasto.com/inference-stack/sglang
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https://www.aguasto.com/inference-stack/tensorrt-llm
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https://www.aguasto.com/inference-stack/dynamo
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https://www.aguasto.com/inference-stack/triton
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https://www.aguasto.com/inference-stack/rocm
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https://www.aguasto.com/inference-stack/ray-serve
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https://www.aguasto.com/inference-stack/kubernetes
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https://www.aguasto.com/learn/model-weights-and-runtime-memory
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https://www.aguasto.com/research/inference-benchmark-methodology
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https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving
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https://www.aguasto.com/learn/choosing-an-inference-serving-framework
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https://www.aguasto.com/learn/from-power-to-inference
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https://www.aguasto.com/insights/dedicated-and-shared-inference-economics
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https://www.aguasto.com/learn/distributed-inference-reliability
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https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures
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https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now
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https://www.aguasto.com/learn/what-is-a-modular-ai-data-center
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https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning
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https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces
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https://www.aguasto.com/research/electricity-availability-and-time-to-power
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https://www.aguasto.com/learn/kv-cache-capacity-and-policy
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https://www.aguasto.com/learn/continuous-batching-and-latency
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https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones
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https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion
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https://www.aguasto.com/learn/data-center-cooling-air-liquid-dry-hybrid
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https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal
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https://www.aguasto.com/learn/measuring-pue-wue-and-ai-compute-efficiency
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https://www.aguasto.com/research/environmental-tradeoffs-and-infrastructure-reuse
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https://www.aguasto.com/learn/selecting-enterprise-inference-gpu-systems
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https://www.aguasto.com/learn/rack-scale-ai-networking-boundaries
How to use
Claude Desktop or claude.ai: add a custom connector (Customize → Connectors; on Team and Enterprise plans an owner adds it under Organization settings → Connectors) with this server URL: https://www.aguasto.com/mcp/docs
Cursor or Cline: add to your MCP config:
{
"mcpServers": {
"playwright_mcp_server": {
"url": "https://www.aguasto.com/mcp/docs",
"transport": "http"
}
}
}