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EcoCompute Should-I-Quantize Estimator MCP server

online · 100% uptime
by hongping-zh·io.github.hongping-zh/ecocompute-estimator·v1.0.0

Energy impact of LLM quantization, calibrated to measured GPU data. Every answer states its basis.

A91/100grade A
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A91/100

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EcoCompute Should-I-Quantize Estimator tools (4)

write = sends, deletes, buys or posts
  • estimate_quantization_energyFree

    Estimate the task-level energy change (ΔE% vs FP16) of weight-only quantization (NF4 or INT8) for a model of a given size on a GPU architecture class, from measured-anchored curves. The answer carries basis (measured/interpolated/estimated), confidence and evidence boundaries.

  • get_quantization_crossoverFree

    Get the model-size crossover for NF4/INT8 per architecture: below it quantization costs energy, above it saves. Crossovers belong to a card + software stack, not an architecture — the answer says so.

  • list_data_coverageFree

    List what is actually measured (GPU architecture × precision × size range, NVML direct), what is borrowed/estimated, and where the gaps are. Use this before trusting an estimate at the edges of coverage, and to answer 'is there data for my GPU?' honestly.

  • search_measurementsFree

    Search the measured anchors by model name, GPU name, architecture or size. Returns ΔE% vs FP16 per anchor with run counts — the raw evidence behind the curves.

Public scan report

scanner v0.1.10 · 2026-10-24 · same rubric, same numbers if you re-run it

no findings
  • –Code scanremote-only server, no package to scann/a
  • Live reliabilityremote reachable in 221ms20/20
  • Tool poisoning4 tool descriptions checked15/15
  • Auth qualityopen endpoint, read-only tools10/15
  • Maintenancelast push 0 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
Overall 91/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How EcoCompute Should-I-Quantize Estimator compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
91/100
higher than 88% of 4,460 AI and agents servers
Tools
4
the typical AI and agents server has 6
Tools that change things
0%
typical: 0%. Fewer write tools means less an agent can break
Uptime
100%
of our recent checks; the typical remote AI and agents server answers 100%
Response time
259 ms
faster than 55% of 2,331 AI and agents servers

What its tools let an agent do: 4 read

Install EcoCompute Should-I-Quantize Estimator in Claude Code, Cursor or VS Code

claude mcp add --transport http ecocompute-estimator https://ecocompute-estimator.zhanghongping1982.workers.dev/mcp
Add to Cursor

EcoCompute Should-I-Quantize Estimator: common questions

Is EcoCompute Should-I-Quantize Estimator MCP server safe?
Yes, by our scan: it is graded A (91/100). Read the EcoCompute Should-I-Quantize Estimator safety report
How do I install EcoCompute Should-I-Quantize Estimator?
It runs remotely at ecocompute-estimator.zhanghongping1982.workers.dev. Add it to Claude Code, Cursor, VS Code or Claude Desktop with the snippets above, or call it through the mcp.market gateway without installing anything.
Does EcoCompute Should-I-Quantize Estimator need an API key?
Not as far as the registry entry and our scan can tell: no credentials are declared or required.
Is EcoCompute Should-I-Quantize Estimator maintained?
The last commit was 11 days ago (2026-10-24). The latest release is v1.0.0.
Is EcoCompute Should-I-Quantize Estimator up?
100% of our last 28 checks got an answer. We check remote servers about four times a day.
What can I use instead of EcoCompute Should-I-Quantize Estimator?
Servers from other publishers that do the same job: mcptoon MCP server, Godot MCP server and Guaardvark MCP server.
How does EcoCompute Should-I-Quantize Estimator compare with other AI and agents MCP servers?
safety score 91/100 (higher than 88% of 4,460 AI and agents servers); tools 4 (the typical AI and agents server has 6); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.

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