aasagenticawesomeskills__inspect_selection_evidence
Validate a canonical selection-evidence sidecar against a manifest and the active verified catalog without writing files or judging the agent's semantic choices.
This record as markdown: /tools/ai-rokha-rokha/aasagenticawesomeskills--inspect-selection-evidence.md
What aasagenticawesomeskills__inspect_selection_evidence does on Rokha
AI agents call aasagenticawesomeskills__inspect_selection_evidence to retrieve information from Rokha without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
evidence | object | Yes | |
manifest | object | Yes |
Parameters from the server's own tool schema.
Why aasagenticawesomeskills__inspect_selection_evidence is rated Low
Tool validates selection-evidence against manifest and catalog; read-only inspection with no side effects.
From the tool's definition Validate against manifest and catalog without writing files
Risk signalsAccepts URL/endpoint input (manifest.targets[].host) · High parameter count (19 properties)
Attacks that exploit this kind of access
The rule that runs aasagenticawesomeskills__inspect_selection_evidence safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Rokha, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For aasagenticawesomeskills__inspect_selection_evidence, this is the rule to start with:
aasagenticawesomeskills__inspect_selection_evidence is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Rokha, apply this rule, and every aasagenticawesomeskills__inspect_selection_evidence call is checked against it from then on.
Questions about aasagenticawesomeskills__inspect_selection_evidence
Validate a canonical selection-evidence sidecar against a manifest and the active verified catalog without writing files or judging the agent's semantic choices. It is categorised as a Read tool in the Rokha MCP Server, which means it retrieves data without modifying state.
aasagenticawesomeskills__inspect_selection_evidence accepts 2 parameters: evidence, manifest. Required: evidence, manifest. The full parameter table on this page comes from the server's own tool schema.
Register the Rokha MCP server in PolicyLayer and add a rule for aasagenticawesomeskills__inspect_selection_evidence: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Rokha. Nothing to install.
aasagenticawesomeskills__inspect_selection_evidence is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the aasagenticawesomeskills__inspect_selection_evidence rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for aasagenticawesomeskills__inspect_selection_evidence. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
aasagenticawesomeskills__inspect_selection_evidence is provided by the Rokha MCP server (https://rokha.ai/mcp/jsonrpc). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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