export_ai_log_artifacts
[MAINTENANCE] c0168425 — Read-only: project-scoped, receipted export of stored ai_log artifacts (meridian.artifact_store — the local-first, content-addressed blob store an ExecutionEvent payload can point to via artifact_ref instead of inlining large content). Pass content_hashes to export an exp...
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What export_ai_log_artifacts does on Meridian
AI agents use export_ai_log_artifacts to create or update resources in Meridian, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Meridian environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
project_id | string | — | |
project_name | string | — | Project name — an alternative to project_id; resolved to the id internally. project_id wins if both are given. |
content_hashes | array | — | Explicit subset of 'sha256:...' hashes to export. Omit to export every artifact stored for the project. |
Parameters from the server's own tool schema.
Why export_ai_log_artifacts is rated Medium
An AI agent can call export_ai_log_artifacts faster than any human can review: one bad instruction and it creates or modifies resources in Meridian by the hundred, each call as confident as the last.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs export_ai_log_artifacts safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Meridian, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For export_ai_log_artifacts, this is the rule to start with:
export_ai_log_artifacts stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Meridian, apply this rule, and every export_ai_log_artifacts call is checked against it from then on.
Questions about export_ai_log_artifacts
[MAINTENANCE] c0168425 — Read-only: project-scoped, receipted export of stored ai_log artifacts (meridian.artifact_store — the local-first, content-addressed blob store an ExecutionEvent payload can point to via artifact_ref instead of inlining large content). Pass content_hashes to export an explicit subset (sha256:... values) — every requested hash must exist for this project, or the call errors rather than silently returning a shorter list; omit it to export every artifact currently stored for the project. Returns {project_id, exported_at, artifact_count, total_size, artifacts, export_hash} — each artifact entry includes its metadata plus base64-encoded content. export_hash covers the metadata only (not the base64 payloads) so it stays cheap to verify. It is categorised as a Write tool in the Meridian MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
export_ai_log_artifacts accepts 3 parameters: project_id, project_name, content_hashes. The full parameter table on this page comes from the server's own tool schema.
Register the Meridian MCP server in PolicyLayer and add a rule for export_ai_log_artifacts: 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 Meridian. Nothing to install.
export_ai_log_artifacts is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the export_ai_log_artifacts 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 export_ai_log_artifacts. 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.
export_ai_log_artifacts is provided by the Meridian MCP server (@meridianmcp/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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