MCP server and CLI that resolves config/env-var cascade across .env files, docker-compose, and Kubernetes — tells AI agents what a variable actually evaluates to, and why.
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resolve_variableResolves what a config/env variable actually evaluates to across dotenv, docker-compose, and Kubernetes sources, and which source wins.Resolves what a config/env variable actually evaluates to across dotenv, docker-compose, and Kubernetes sources, and which source wins.
No input schema was published for this tool.
trace_variableLists every place a variable is defined or read across dotenv, compose, k8s, and code, and what wins in each discovered environment/service/workload context.Lists every place a variable is defined or read across dotenv, compose, k8s, and code, and what wins in each discovered environment/service/workload context.
No input schema was published for this tool.
impact_previewPredicts which environments/services/workloads are actually affected if a variable's value in one file is changed (or removed) — including which contexts are shielded by a higher-precedence source.Predicts which environments/services/workloads are actually affected if a variable's value in one file is changed (or removed) — including which contexts are shielded by a higher-precedence source.
No input schema was published for this tool.
config_manifestCompressed, project-wide summary of every config variable found — sources, domains, and risk hotspots (likely secrets in tracked files, variables read in code with no source). Load once per session instead of repeated resolve_variable calls.Compressed, project-wide summary of every config variable found — sources, domains, and risk hotspots (likely secrets in tracked files, variables read in code with no source). Load once per session instead of repeated resolve_variable calls.
No input schema was published for this tool.
diff_environmentsResolves every variable under two different environment/service/workload selectors and reports what differs between them.Resolves every variable under two different environment/service/workload selectors and reports what differs between them.
No input schema was published for this tool.
5 of 5 tools published a description.
Tool names and descriptions are written by the publisher and shown verbatim as inert text. They are the strings an MCP client passes to a model, so Forge scans them for prompt-injection patterns — any finding appears with the security scan above. “Privileged” is a keyword match on the tool name, not an audit of what the tool does: a benign-sounding name can still do anything.
MCP server and CLI that resolves config/env-var cascade across .env files, docker-compose, and Kubernetes — tells AI agents what a variable actually evaluates to, and why.
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