Pixelle-MCP

Name: Pixelle-MCP
Rating: 2.88 (1 reviews)
Author: AIDC-AI

#3144 · by AIDC-AI

framework

2.88/ 5.00

cautionBeta

Mar 3, 2026 at 7:21 AM6 signals analysedNo manual reviews · fully automated

Trust Signal Breakdown

medium23 sub-signals across 6 dimensions

CVEs, dependency health, and supply chain integrity

0 of 3 sub-signals with data

Known CVEsno data—

Weight redistributed to sub-signals with data

Dependency Healthno data—

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Supply Chainno data—

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Uptime, latency, error rates, and incident history

4 of 4 sub-signals with data

Uptime35%5.0

100.00% over 3 checks

via Health checks

Response Latency25%4.0

p99: 214ms, p50: 160ms

via Health checks

Error Rate20%1.0

33.33% error rate (1/3)

via Health checks

Incident History20%3.0

2 incidents in last 90 days

via Incidents table

Commit recency, release cadence, issue response, CI/CD

4 of 4 sub-signals with data

Commit Recency30%3.0

via GitHub

Release Cadence25%2.0

via GitHub

Issue Response20%2.0

via GitHub

CI/CD Presence25%2.0

via GitHub Actions

Downloads, stars, dependents, and growth trajectory

1 of 4 sub-signals with data

Download Volumeno data—

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GitHub Stars100%2.0

927 stars

via GitHub

Dependent Packagesno data—

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Growth Trendno data—

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License, documentation, security policy, changelog

4 of 4 sub-signals with data

Open Source30%5.0

Public repo with OSI-approved license (mit)

via GitHub

Documentation25%5.0

Docs site present with comprehensive README (>2000 bytes + examples)

via GitHub

Security Policy20%2.0

No SECURITY.md found

via GitHub

Changelog25%2.0

No CHANGELOG.md and no releases found

via GitHub

Track record, org maturity, community standing

0 of 4 sub-signals with data

Track Recordno data—

Weight redistributed to sub-signals with data

Org Maturityno data—

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Community Standingno data—

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Cross-Platformno data—

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About this score

Scored across 23 sub-signals in 6 dimensionsScoring engine v1 (beta) — actively being expandedPhase 1: Core sub-signal architecture (live)Phase 2: Permission scope & expanded collection (in progress)

Trust AssessmentAI Assessment

An omnimodal AIGC framework that seamlessly converts ComfyUI workflows into MCP tools with zero code, enabling full-modal support for Text, Image, Sound, and Video generation with Chainlit-based web interface.

Package Availability (30d)

100.00%

p50: 160ms · p99: 214ms

Avg Latency

130ms

averaged across 30d health checks

Weekly Downloads

—

no package registry data

Transparency & Compliance4/5 passed

✓

Open Source CodePublic repository on GitHub