Jellyfish MCP Server
jellyfish-mcp-server
npm
v1.2.4
Source verified
Published by jellyfish-ai — publish provenance cryptographically ties this package to that repository. That is proof of origin, not an official vendor package.
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 3 = 97. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −3 | capability blast radius (moderate) — client exposure if the model is manipulated |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 0
Tools 35
Each tool and what it can reach — statically extracted from the published source.
get_deliverableingests untrusted inputai_company_adoption_analyticsno sensitive capabilityai_company_impact_analyticsno sensitive capabilityai_person_adoptionno sensitive capabilityai_person_adoption_analyticsno sensitive capabilityai_team_adoption_analyticsno sensitive capabilityai_team_impact_analyticsno sensitive capabilityallocations_by_investment_categoryno sensitive capabilityallocations_by_investment_category_personno sensitive capabilityallocations_by_investment_category_teamno sensitive capability
Show 25 more tools ↓
allocations_by_personno sensitive capabilityallocations_by_teamno sensitive capabilityallocations_by_work_categoryno sensitive capabilityallocations_by_work_category_personno sensitive capabilityallocations_by_work_category_teamno sensitive capabilityallocations_filter_fieldsno sensitive capabilityallocations_summary_by_investment_categoryno sensitive capabilityallocations_summary_by_work_categoryno sensitive capabilityapi_schemano sensitive capabilitycompany_metricsno sensitive capabilitydeliverable_scope_and_effort_historyno sensitive capabilitydevex_insights_by_teamno sensitive capabilityget_articleno sensitive capabilityget_current_dateno sensitive capabilitylist_engineersno sensitive capabilitylist_teamsno sensitive capabilityperson_metricsno sensitive capabilitysearch_articlesno sensitive capabilitysearch_deliverablesno sensitive capabilitysearch_peopleno sensitive capabilitysearch_teamsno sensitive capabilityteam_metricsno sensitive capabilityteam_sprint_summaryno sensitive capabilityunlinked_pull_requestsno sensitive capabilitywork_categoriesno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (35 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v1.2.4 latest |
A 97/100 | 0 | 1.10.0 | 2026-07-25 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
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