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MCP live MCP 2025-11-25 http

Playwright MCP Server

mcp.so:altssdata/aguasto-research
Watch this dependency Share evidence report
Uptime
100.0%
3 direct probes · 30d
Response
802ms
last probe
Tools
2
callable
Resources
90
readable
Prompts
0
available
Score: 90/100
Handshake verified by our own probe.
pass Answers the MCP handshake 50/50
The handshake and the list calls succeeded on the primary URL.
pass Lists tools, resources or prompts 30/30
2 tools, 90 resources, 0 prompts returned.
pass 3 or more catalogue items 10/10
92 items in total; 3 or more earns these points.
fail Describes itself 0/10
0 characters; more than 30 earns these points.
How to earn +10 points
Describe the server in one or two sentences
Set description in your server.json (or the registry listing) to say what the server does and for whom.

Tools · 2

search_pages

Find published Aguasto page titles, descriptions and canonical references. Follow the page for full sourced evidence.

get_page

Retrieve a published page's description, date, canonical URL and Markdown link. Not the full article text.

Resources · 90

API Versioning & Deprecation Policy

Published page description and links; follow canonical HTML for full content.

https://www.aguasto.com/developers/versioning
Developer Sandbox & Synthetic API Tests

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https://www.aguasto.com/sandbox
Aguasto | Modular AI Infrastructure, GPU Compute & Inference

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https://www.aguasto.com/
Infrastructure

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https://www.aguasto.com/infrastructure
GPU Cloud

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https://www.aguasto.com/gpu-cloud
AI Inference

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https://www.aguasto.com/ai-inference
Development & Investment

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https://www.aguasto.com/development
Company

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https://www.aguasto.com/company
Contact

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https://www.aguasto.com/contact
Privacy

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https://www.aguasto.com/privacy
Public Research Terms of Use

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https://www.aguasto.com/terms
AI Compute Research Topics

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https://www.aguasto.com/topics
GPU systems & local clusters

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https://www.aguasto.com/topics/gpu-systems
Model serving & inference

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https://www.aguasto.com/topics/model-serving
Modular infrastructure & resources

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https://www.aguasto.com/topics/modular-infrastructure
Compute economics & evidence

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https://www.aguasto.com/topics/compute-economics
AI Compute & Infrastructure Glossary

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https://www.aguasto.com/glossary
Research Sources & Methodologies

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https://www.aguasto.com/methodologies
Developer API, MCP & SDK Documentation

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https://www.aguasto.com/developers
Aguasto Research & Resources

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https://www.aguasto.com/resources
GPU Library

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https://www.aguasto.com/gpus
Model Index

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https://www.aguasto.com/models
Inference Stack Library

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https://www.aguasto.com/inference-stack
AI Infrastructure Engineering Guides

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https://www.aguasto.com/learn
AI Infrastructure Insights

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https://www.aguasto.com/insights
AI Infrastructure Research

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https://www.aguasto.com/research
Public Infrastructure Case Analyses

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https://www.aguasto.com/research/cases
Why Modular AI Data Centers?

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https://www.aguasto.com/infrastructure/why-modular
Cooling & Resource Efficiency

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https://www.aguasto.com/infrastructure/cooling-resource-efficiency
OEM & Strategic Collaboration

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https://www.aguasto.com/partners
Compare GPU & Rack References

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https://www.aguasto.com/gpus/compare
Compare Open-Weight Model References

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https://www.aguasto.com/models/compare
AI Infrastructure Engineering Calculators

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https://www.aguasto.com/tools
The Growing Friction in Hyperscale Data Center Development

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https://www.aguasto.com/research/hyperscale-development-friction
NVIDIA H100 SXM

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https://www.aguasto.com/gpus/h100
NVIDIA H200 SXM

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https://www.aguasto.com/gpus/h200
NVIDIA B200 SXM

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https://www.aguasto.com/gpus/b200
NVIDIA B300

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https://www.aguasto.com/gpus/b300
NVIDIA GB200 NVL72

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https://www.aguasto.com/gpus/gb200-nvl72
NVIDIA GB300 NVL72

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https://www.aguasto.com/gpus/gb300-nvl72
NVIDIA Vera Rubin NVL72

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https://www.aguasto.com/gpus/vera-rubin-nvl72
AMD Instinct MI300X

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https://www.aguasto.com/gpus/mi300x
AMD Instinct MI325X

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https://www.aguasto.com/gpus/mi325x
AMD Instinct MI355X

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https://www.aguasto.com/gpus/mi355x
AMD Instinct MI455X

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https://www.aguasto.com/gpus/mi455x
AMD Helios

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https://www.aguasto.com/gpus/helios
GLM-4.5

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https://www.aguasto.com/models/glm-4-5
Qwen3-235B-A22B

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https://www.aguasto.com/models/qwen3-235b-a22b
DeepSeek-R1

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https://www.aguasto.com/models/deepseek-r1
Llama 3.3 70B Instruct

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https://www.aguasto.com/models/llama-3-3-70b-instruct
Gemma 3 27B IT

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https://www.aguasto.com/models/gemma-3-27b-it
gpt-oss-120b

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https://www.aguasto.com/models/gpt-oss-120b
Kimi-K2-Instruct

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https://www.aguasto.com/models/kimi-k2-instruct
GLM-5.3

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https://www.aguasto.com/models/glm-5-3
Qwen3.8-27B

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https://www.aguasto.com/models/qwen3-8-27b
DeepSeek-V4.1-Flash

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https://www.aguasto.com/models/deepseek-v4-1-flash
Llama 4 Scout 17B-16E Instruct

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https://www.aguasto.com/models/llama-4-scout-17b-16e-instruct
Gemma 4 31B IT

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https://www.aguasto.com/models/gemma-4-31b-it
Kimi K3

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https://www.aguasto.com/models/kimi-k3
vLLM

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https://www.aguasto.com/inference-stack/vllm
SGLang

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https://www.aguasto.com/inference-stack/sglang
TensorRT-LLM

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https://www.aguasto.com/inference-stack/tensorrt-llm
NVIDIA Dynamo

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https://www.aguasto.com/inference-stack/dynamo
NVIDIA Triton Inference Server

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https://www.aguasto.com/inference-stack/triton
AMD ROCm

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https://www.aguasto.com/inference-stack/rocm
Ray Serve

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https://www.aguasto.com/inference-stack/ray-serve
Kubernetes GPU orchestration — NVIDIA device plugin

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https://www.aguasto.com/inference-stack/kubernetes
Model weights are only the beginning of the memory budget

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https://www.aguasto.com/learn/model-weights-and-runtime-memory
Why tokens per second is not enough to benchmark inference

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https://www.aguasto.com/research/inference-benchmark-methodology
Prefill, decode and the cost of separating them

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https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving
Choosing between vLLM, SGLang and TensorRT-LLM

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https://www.aguasto.com/learn/choosing-an-inference-serving-framework
From Power to Inference—Building a Distributed AI Compute Network

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https://www.aguasto.com/learn/from-power-to-inference
Dedicated compute and shared inference: compare the whole workload

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https://www.aguasto.com/insights/dedicated-and-shared-inference-economics
Distributed inference reliability begins with the failure boundary

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https://www.aguasto.com/learn/distributed-inference-reliability
Core Scientific: reading contracted, energized and billed capacity

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https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures
Why Modular AI Infrastructure Matters Now

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https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now
What Is a Modular AI Data Center?

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https://www.aguasto.com/learn/what-is-a-modular-ai-data-center
How Modular AI Data Centers Are Built and Commissioned

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https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning
Liquid Cooling for High-Density GPU Systems: Follow the Interfaces

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https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces
Why Electricity Availability Shapes AI Infrastructure Deployment

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https://www.aguasto.com/research/electricity-availability-and-time-to-power
What Is KV Cache? Capacity, Reuse and Isolation

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https://www.aguasto.com/learn/kv-cache-capacity-and-policy
Continuous Batching: Throughput Without Hiding Latency

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https://www.aguasto.com/learn/continuous-batching-and-latency
Applied Digital: Reading the Polaris Forge 1 Delivery Milestones

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https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones
Crusoe Abilene: Separate Delivered Infrastructure from Expansion Plans

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https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion
Data Center Cooling: Choosing Air, Liquid, Dry and Hybrid Architectures

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https://www.aguasto.com/learn/data-center-cooling-air-liquid-dry-hybrid
Can Data Centers Reduce Water Consumption? Define the Water Boundary First

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https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal
Measuring PUE, WUE and AI Compute Efficiency at the Right Boundary

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https://www.aguasto.com/learn/measuring-pue-wue-and-ai-compute-efficiency
The Environmental Trade-offs of High-Density AI Infrastructure

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https://www.aguasto.com/research/environmental-tradeoffs-and-infrastructure-reuse
Selecting GPU Systems for Enterprise Inference

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https://www.aguasto.com/learn/selecting-enterprise-inference-gpu-systems
Networking Rack-Scale AI Systems: Define the Communication Boundary

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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"
    }
  }
}