Stepsecurity MCP Server
@stepsecurity/stepsecurity-mcp
npm
v0.1.0
Published by @stepsecurity — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
Model Context Protocol server for StepSecurity APIs
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 − 5 = 95. 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 |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
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 30
Each tool and what it can reach — statically extracted from the published source.
list_secrets_in_build_logreads sensitive dataanalyze_anomalous_calls_by_processno sensitive capabilitycheck_ioc_in_baselineno sensitive capabilitycheck_npm_package_exposureno sensitive capabilitycheck_npm_package_on_dev_machinesno sensitive capabilitycheck_pypi_package_exposureno sensitive capabilitycheck_python_package_on_dev_machinesno sensitive capabilitycreate_suppression_ruleno sensitive capabilitydelete_suppression_ruleno sensitive capabilitydescribe_capabilitiesno sensitive capability
Show 20 more tools ↓
find_endpoint_calls_in_tenantno sensitive capabilityfind_repos_using_endpointno sensitive capabilityget_my_tenantno sensitive capabilityget_suppression_ruleno sensitive capabilityget_threat_incidentno sensitive capabilitylist_anomalous_network_callsno sensitive capabilitylist_blocked_domain_callsno sensitive capabilitylist_detectionsno sensitive capabilitylist_github_api_calls_in_runno sensitive capabilitylist_https_outbound_callsno sensitive capabilitylist_imposter_commit_detectionsno sensitive capabilitylist_recent_workflow_runsno sensitive capabilitylist_suppression_rulesno sensitive capabilitylist_suspicious_process_eventsno sensitive capabilitylist_tenant_github_orgsno sensitive capabilitylist_threat_incidentsno sensitive capabilitypingno sensitive capabilitypreview_suppression_ruleno sensitive capabilitysearch_action_usageno sensitive capabilityupdate_suppression_ruleno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (30 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 |
|---|---|---|---|---|
v0.1.0 latest |
A 95/100 | 0 | 1.13.0 | 2026-09-07 |
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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