Krauncher analyzer
PYPI · KRAUNCHER-MCP · SCANNED SEP 20
How long a GPU job will run and cost, before running it — static code analysis, never executed.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 1 of 39 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 37 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability52
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 1164 tokens (~1164/item across 1 items; 1 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management80
- Stability observed for 24 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage67
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 1 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 2 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
How do I install the Krauncher analyzer MCP server?
Krauncher analyzer runs locally as a PyPI package, launched with uvx krauncher-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · krauncher-mcp
claude mcp add ilya-a-sergeyev-ger-krauncher-mcp -- uvx krauncher-mcp
{
"mcpServers": {
"ilya-a-sergeyev-ger-krauncher-mcp": {
"command": "uvx",
"args": [
"krauncher-mcp"
]
}
}
} {
"servers": {
"ilya-a-sergeyev-ger-krauncher-mcp": {
"command": "uvx",
"args": [
"krauncher-mcp"
]
}
}
} codex mcp add ilya-a-sergeyev-ger-krauncher-mcp -- uvx krauncher-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"ilya-a-sergeyev-ger-krauncher-mcp": {
"type": "local",
"command": [
"uvx",
"krauncher-mcp"
],
"enabled": true
}
}
} openclaw mcp add ilya-a-sergeyev-ger-krauncher-mcp --command uvx --arg krauncher-mcp
mcp_servers:
ilya-a-sergeyev-ger-krauncher-mcp:
command: "uvx"
args: ["krauncher-mcp"] {
"McpServers": {
"ilya-a-sergeyev-ger-krauncher-mcp": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"krauncher-mcp"
]
}
}
} assistant mcp add ilya-a-sergeyev-ger-krauncher-mcp -t stdio -c uvx -a krauncher-mcp
{
"mcpServers": {
"ilya-a-sergeyev-ger-krauncher-mcp": {
"command": "uvx",
"args": [
"krauncher-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 20 Sept 26 −3
- Stability: pass → 0.80 functional
- 19 Sept 26 +1
- Stability: 0.97 → pass security
- 17 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 15 Sept 26 +16
- Malware scan: unverified → pass ▲ security
- 14 Sept 26 −15
- Malware scan: pass → unverified ▼ security
- 13 Sept 26 −3
- Stability: pass → 0.80 functional
- 12 Sept 26 +1
- Stability: 0.97 → pass security
- 11 Sept 26 −1
- Stability: pass → 0.97 functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed pypi/krauncher-mcp@0.5.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 39 packages
| Packages resolved | 39 |
|---|---|
| No linked repository | 1 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
estimate_gpu_time_and_cost ~938
Runtime, cost and performance analysis of a GPU job, before running it. Use it to predict how long a training or inference job will take, find what holds its performance back, see whether the GPU is utilized or left waiting on the host, and pick the run parameters that make the code faster or cheaper. Numbers come from a corpus of measured runs — a runtime reasoned out of source is routinely wrong by several times. The code is analyzed, never executed: change a parameter, re-estimate, keep the cheaper variant — zero GPU-seconds. Returns, on a fixed reference card (RTX PRO 6000 WS): compute_sec / setup_sec / io_sec, min_vram_gb / min_disk_gb, cpu_only, confidence (0-1), analysis_method, findings (what the analyzer read from the code), plus knobs / spread / spread_reason / calibration_basis / iterations / iteration_basis: `knobs` is the shortlist worth re-estimating — change their VALUES ONLY, not the model, architecture, dataset or procedure. `value: null` means it is not a literal in this source (it arrives as an argument, a config entry or an env var), so name it in the code and estimate again. `same_work: false` (epochs, steps, sequence length) shrinks the job itself: report that as a change of task, never as a saving. `spread` is how far the same code scatters across real hosts: 1.5 means the slow end of the measured population runs about 1.5x the estimate. It is a measured factor, not doubt about the reading — confidence 1.0 with a large spread says the GPU is not what governs this job's time, the card waits on the host. The lever is GPU utilization: find what leaves the card idle (data loaded in the main process, per-item preprocessing, synchronous transfers, a batch too small to fill it). `spread_reason` names it. `calibration_basis` is "calibrated", "extrapolated" (a neighbour answered) or "uncalibrated" (read the seconds as an order of magnitude). `iterations` is the step count the whole estimate scales with, and `iteration_basis` is where it came fro…
| Name | Type | Req | Description |
|---|---|---|---|
| code | string | yes | – |
| run_args | – | – | – |
No output schema declared.
No examples provided.
What is the Krauncher analyzer MCP server?
Krauncher analyzer is an MCP server listed in the public MCP registry as io.github.Ilya-a-sergeyev-ger/krauncher-mcp. How long a GPU job will run and cost, before running it, static code analysis, never executed. This page covers its PyPI package (krauncher-mcp).
Is the Krauncher analyzer MCP server safe to use?
Krauncher analyzer scores 75 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Krauncher analyzer MCP server expose?
Krauncher analyzer exposes 1 tool: estimate_gpu_time_and_cost. Their descriptions and schemas cost roughly 938 tokens of context every time the server is loaded.
Is the Krauncher analyzer MCP server still maintained?
Krauncher analyzer is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Krauncher analyzer MCP server under?
Krauncher analyzer declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.