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MCP server exposing the Predge Whale Data x402 API (Polymarket whale trades + smart-money signals) as tools. Pays USDC per call under the hood via an x402 client; the operator supplies a funded buyer key.
The publisher performed an authenticated act of publication — proving control of a namespace in the official MCP registry, or appearing in a first-party catalogue an operator curates. That establishes WHO published it. It is not a security finding, and attested artifacts carry Flagged verdicts too.
github_release: https://github.com/predgeAI/whale-data-mcp, 0 findings
Choose your runtime: the CLI writes the mcpServers config entry for that target.
nerlo install --target claude-code -- @predge/whale-data-mcp
Install respects the composite badge: Clean proceeds, Caution prompts for confirmation, and Flagged is refused. Write operations require an API token.
8 of 11 scanners examined this package. The rest do not apply to it — language, ecosystem and packaging decide which analyzers can say anything, and a scanner that cannot examine an artifact reports nothing rather than passing it.
v1.4.0
0 findings · 3.5s
v2.0.11
5 findings · 39.2s
v0.3.26
4 findings · 7.0s
v0.1.0
0 findings · 46.5s
v0.1.0
0 findings · 46.5s
v0.1.0
0 findings · 35.5s
v0.71.0
2 findings · 0.8s
v2.3.8
2 findings · 14.3s
v0.71.0
No container image was published for this artifact. What each scanner covers
0 findings · 0.0s
No report in this scan: capslock, govulncheck.
1 scan on record. Every scan's full results are retained immutably for 24 months.
| Completed | Composite | Change | Scanners | Status |
|---|---|---|---|---|
| Aug 2, 2026 | Caution87 | — | agentshield: completecisco-skill-scanner: completeagent-audit-kit: completenerlo-behavioral: completenerlo-install-instruction: completenerlo-multi-source: completetrivy: completeosv-scanner: completetrivy_image ·n/a: not applicable | completed |
Findings in files this artifact installs stand as reported; a model re-read the rest and said which ones it believes are false positives. This is a second opinion published beside the evidence, not a correction to it: the per-scanner reports above are unchanged, every dismissed finding is still listed there at its original severity, and the score and the Caution badge are computed from those raw severities alone. Nothing below moved them.
Out of 13 findings across every scanner that examined this package.
These findings are in files this artifact installs and are not placeholder values. No false-positive basis can be corroborated for such a finding, so this review never dismisses one; they were not sent to the model and stand at their scanner's severity.
2 GHSA-frvp-7c67-39w9
Files: package-lock.json, /repo/package-lock.json · reported by trivy, osv-scanner
1 AAK-ANTHROPIC-SDK-001
Files: src/index.ts · reported by agent-audit-kit
1 AAK-MCP-018
Files: src/server.ts · reported by agent-audit-kit
1 AAK-OAUTH-3P-001
Files: package.json · reported by agent-audit-kit
1 AAK-SEC-MD-001
Files: SECURITY.md · reported by agent-audit-kit
1 CVE-2026-16221
Files: package-lock.json · reported by trivy
1 DATA_EXFIL_JS_NETWORK
Files: /repo/src/pay.ts · reported by cisco-skill-scanner
1 GHSA-v2hh-gcrm-f6hx
Files: /repo/package-lock.json · reported by osv-scanner
1 HIDDEN_DATA_FILE
Files: /repo/.mcpbignore · reported by cisco-skill-scanner
1 LOW_ANALYZABILITY
Files: /repo · reported by cisco-skill-scanner
1 MANIFEST_MISSING_LICENSE
Files: /repo/SKILL.md · reported by cisco-skill-scanner
1 SOCIAL_ENG_VAGUE_DESCRIPTION
Files: /repo/SKILL.md · reported by cisco-skill-scanner
High severity vulnerabilities and AI SDK usage detected.
The artifact contains high severity vulnerabilities, including CVE-2026-16221 and GHSA-v2hh-gcrm-f6hx, which affect dependencies. Additionally, the artifact uses the Anthropic SDK in `src/index.ts`, which typically involves network egress for API communication and may handle credentials. These findings warrant attention prior to installation.
Concern level assessed by the review: High. This is the review's scale, not the registry badge.
Reviewed by gemini-2.5-flash. The model sees the findings, not your code, and cannot change a score.