Why MCP Needs Run Logs

The operator question was not about a single Amazon workflow: "If an MCP-connected agent calls tools for us, how do we know what happened afterward?"

The answer is a run log. Model Context Protocol (MCP) gives agents a clean way to call tools. Production teams need the durable record around those calls: who asked, which tool ran, what parameters were passed, what policy checks fired, what changed, and who approved the change.

What a model without Atlas gets wrong

That makes the agent hard to trust even when the output is correct.

What Atlas retrieves

Atlas is one of the sources a run log must name. If an agent uses Amazon Agent Atlas to ground an Amazon Marketing Cloud (AMC) query, listing patch, or decision rule, the run log should capture:

• The Atlas corpus version used for the answer.
• The chunk identifiers that grounded the rule.
• The tool inputs, outputs, and policy checks from the MCP server.

The same principle applies to live data called through an MCP server: capture the tool, inputs, outputs, and policy checks in the same run.

For Amazon workflows, the Amazon Ads MCP and Selling Partner MCP provide the live data surfaces. The run log is the connective record that explains why the agent used them.

The agent's working output

A production run log should answer five questions:

{
  "event": "mcp_tool_run",
  "riskTier": "write",
  "initiator": {"user": "required", "workspace": "required"},
  "execution": {"skillId": "required", "skillVersion": "required"},
  "toolCall": {"server": "required", "tool": "required", "parametersHash": "required"},
  "grounding": {"atlasCorpusVersion": "required", "chunkIds": ["required"]},
  "controls": {"checks": ["privacy", "permission", "approval"], "approvalState": "required"},
  "result": {"artifactHash": "required", "destination": "required", "rollbackContext": "required"}
}

If a field cannot be logged, the agent should treat that as a product gap before write access expands.

The footnotes the agent surfaces

Those details are mundane until something goes wrong. Then they are the product.

What happens next

The full workflow runs through the Amazon Agent Data layer: Amazon Ads MCP and Selling Partner MCP supply live Amazon context, Atlas grounds the answer, and Skills make the tool sequence repeatable. The run log records every step so a human can review the decision after the agent acts.

When a workflow graduates from analysis to activation, the run log becomes the audit surface for approval, debugging, and customer trust.

The point

If a tool call cannot be reconstructed, it should not be trusted in production.

Next: use the AMC Skill workflow pattern to decide which tool calls need approval before they write.

{"event":"mcp_tool_run","riskTier":"write","requiredFields":["initiator","tool_parameters","atlas_chunk_ids","approval_state","artifact_hash","rollback_context"]}