Rhoai MCP Server
rhoai-mcp
PyPI
v0.1.1
Published by opendatahub-io — no publish provenance, so origin is unverified, but the source is public: the repository link below is self-declared yet readable, so you can inspect the code before adopting it.
MCP server for Red Hat OpenShift AI
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 − 7 = 93. 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 |
|---|---|
| −6 | capability blast radius (high) — client exposure if the model is manipulated |
| −1 | publisher verification (public source) — no provenance, but the source is public and inspectable |
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 2
high Shell/command execution in server code (evals/reporting/recorder.py)MTC-SRC-002
In the server's implementation (`evals/reporting/recorder.py:38`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: commit = ( subprocess.check_output( ["git", "rev-parse", "--short", "HEAD"],
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server evals/reporting/recorder.py
high Shell/command execution in server code (src/rhoai_mcp/domains/model_registry/auth.py)MTC-SRC-002
In the server's implementation (`src/rhoai_mcp/domains/model_registry/auth.py:114`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: result = subprocess.run( [cli_path, "whoami", "-t"], capture_outp
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server src/rhoai_mcp/domains/model_registry/auth.py
Tools 90
Each tool and what it can reach — statically extracted from the published source.
get_registered_modelingests untrusted inputanalyze_training_failureno sensitive capabilitycheck_deployment_prerequisitesno sensitive capabilitycheck_training_prerequisitesno sensitive capabilitycluster_summaryno sensitive capabilitycreate_data_science_projectno sensitive capabilitycreate_pipeline_serverno sensitive capabilitycreate_runtimeno sensitive capabilitycreate_s3_data_connectionno sensitive capabilitycreate_serving_runtimeno sensitive capability
Show 80 more tools ↓
create_storageno sensitive capabilitycreate_workbenchno sensitive capabilitydelete_data_connectionno sensitive capabilitydelete_data_science_projectno sensitive capabilitydelete_inference_serviceno sensitive capabilitydelete_pipeline_serverno sensitive capabilitydelete_runtimeno sensitive capabilitydelete_storageno sensitive capabilitydelete_training_jobno sensitive capabilitydelete_workbenchno sensitive capabilitydeploy_modelno sensitive capabilitydiagnose_resourceno sensitive capabilityestimate_resourcesno sensitive capabilityestimate_serving_resourcesno sensitive capabilityexplore_clusterno sensitive capabilityfind_benchmarks_by_gpuno sensitive capabilityfix_pvc_permissionsno sensitive capabilityget_catalog_model_artifactsno sensitive capabilityget_cluster_resourcesno sensitive capabilityget_data_connectionno sensitive capabilityget_deployment_configno sensitive capabilityget_example_itemno sensitive capabilityget_inference_serviceno sensitive capabilityget_job_eventsno sensitive capabilityget_job_specno sensitive capabilityget_model_artifactsno sensitive capabilityget_model_benchmarksno sensitive capabilityget_model_endpointno sensitive capabilityget_model_versionno sensitive capabilityget_pipeline_serverno sensitive capabilityget_project_detailsno sensitive capabilityget_resourceno sensitive capabilityget_runtime_detailsno sensitive capabilityget_training_jobno sensitive capabilityget_training_logsno sensitive capabilityget_training_progressno sensitive capabilityget_validation_metricsno sensitive capabilityget_workbenchno sensitive capabilityget_workbench_urlno sensitive capabilitylist_catalog_sourcesno sensitive capabilitylist_data_connectionsno sensitive capabilitylist_data_science_projectsno sensitive capabilitylist_example_itemsno sensitive capabilitylist_inference_servicesno sensitive capabilitylist_model_versionsno sensitive capabilitylist_notebook_imagesno sensitive capabilitylist_registered_modelsno sensitive capabilitylist_resource_namesno sensitive capabilitylist_resourcesno sensitive capabilitylist_serving_runtimesno sensitive capabilitylist_storageno sensitive capabilitylist_tool_categoriesno sensitive capabilitylist_training_jobsno sensitive capabilitylist_training_runtimesno sensitive capabilitylist_workbenchesno sensitive capabilitymanage_checkpointsno sensitive capabilitymanage_resourceno sensitive capabilitymulti_resource_statusno sensitive capabilityprepare_model_deploymentno sensitive capabilityprepare_trainingno sensitive capabilityproject_summaryno sensitive capabilityrecommend_modelno sensitive capabilityrecommend_serving_runtimeno sensitive capabilityresource_statusno sensitive capabilityresume_training_jobno sensitive capabilityrun_container_training_jobno sensitive capabilityset_model_serving_modeno sensitive capabilitysetup_hf_credentialsno sensitive capabilitysetup_nfs_storageno sensitive capabilitysetup_training_runtimeno sensitive capabilitysetup_training_storageno sensitive capabilitystart_workbenchno sensitive capabilitystop_workbenchno sensitive capabilitysuggest_toolsno sensitive capabilitysuspend_training_jobno sensitive capabilitytest_model_endpointno sensitive capabilitytrainno sensitive capabilitytrainingno sensitive capabilityvalidate_training_configno sensitive capabilitywait_for_job_completionno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (90 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 |
|---|---|---|---|---|
v0.1.1 latest |
A 93/100 | 2 | 1.13.0 | 2026-08-25 |
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.
npx mcptrustchecker scan rhoai-mcp --online --registry pypiMore in Developer Tools
Hotel booking MCP server — 300K+ properties, real confirmation numbers, loyalty programs. Builders monetize every booking via Stripe Connect. The first MCP server that completes real hotel reservations inside AI conversations.
Manage AdGuard Home through AI assistants
Read-only Azure DevOps for MCP clients using only your existing browser session — no PAT, no Azure CLI. Browse work items, pull requests, comments, attachments and Artifacts feeds across every project, repo and feed you can access.
MCP server for Adobe Experience Manager Assets integration development
Servidor MCP para el tiempo oficial de España (API pública OpenData de AEMET). Predicción, observación y avisos como herramientas MCP tipadas.
A standalone MCP stdio bridge for Affinity by Canva's local MCP SSE server.