get_agent_artifact
Returns an agent-onboarding artifact shipped with a store template: system prompt, Agent Skills SKILL.md or MCP-config snippet. Pass slug for the RAW template artifact (before install, {{slot:...}} placeholders intact) or display_id for the display-bound artifact with placeholders resolved agains...
This record as markdown: /tools/de-agentview-agentview-mcp/get-agent-artifact.md
What get_agent_artifact does on agentView
AI agents call get_agent_artifact to retrieve information from agentView 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 |
|---|---|---|---|
key | string | Yes | Artifact key, e.g. 'bot-system-prompt', 'agent-skill', 'mcp-config'. |
slug | string | — | Template slug for the raw artifact. Provide slug OR display_id. |
display_id | string | — | Display profile ID for the placeholder-resolved artifact. If both slug and display_id are given, display_id wins (substituted body). |
access_token | string | — | Optional bearer token; prefer session_request_id. |
session_request_id | string | — | Session handle from create_auth_session; pass it on every authenticated call. |
Parameters from the server's own tool schema.
Why get_agent_artifact is rated Low
Even though get_agent_artifact only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Risk signalsHandles credentials or secrets (access_token)
Attacks that exploit this kind of access
The rule that runs get_agent_artifact safely
PolicyLayer is an MCP gateway: it sits between your AI agents and agentView, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For get_agent_artifact, this is the rule to start with:
get_agent_artifact 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 agentView, apply this rule, and every get_agent_artifact call is checked against it from then on.
Questions about get_agent_artifact
Returns an agent-onboarding artifact shipped with a store template: system prompt, Agent Skills SKILL.md or MCP-config snippet. Pass slug for the RAW template artifact (before install, {{slot:...}} placeholders intact) or display_id for the display-bound artifact with placeholders resolved against installed slots (ready to save, e.g. into ~/.claude/skills/). Discover keys via get_store_template_details (agentArtifacts array). Template mode needs no auth; display mode requires content scope and display ownership. It is categorised as a Read tool in the agentView MCP Server, which means it retrieves data without modifying state.
get_agent_artifact accepts 5 parameters: key, slug, display_id, access_token, session_request_id. Required: key. The full parameter table on this page comes from the server's own tool schema.
Register the agentView MCP server in PolicyLayer and add a rule for get_agent_artifact: 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 agentView. Nothing to install.
get_agent_artifact 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 get_agent_artifact 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 get_agent_artifact. 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.
get_agent_artifact is provided by the agentView MCP server (https://agentview.de/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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