Read a single step from a workflow by step ID. Cheap alternative to get_workflow (typically ~1KB vs 50-200KB for a full workflow). This returns the configured step definition only. To debug the actual prompt used in a specific execution, use list_timelines then get_timeline for that step invocati...
Part of the Agentled server.
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AI agents call get_step to retrieve information from Agentled without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though get_step only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"get_step": {}
}
}See the full Agentled policy for all 119 tools.
These attack patterns abuse exactly the kind of access get_step gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Read a single step from a workflow by step ID. Cheap alternative to get_workflow (typically ~1KB vs 50-200KB for a full workflow). This returns the configured step definition only. To debug the actual prompt used in a specific execution, use list_timelines then get_timeline for that step invocation and inspect metadata.computedPrompt. Use this before editing dictionary-shaped fields (stepInputData.fieldUpdates, responseStructure, knowledgeSync.fieldMapping, agent.workers) so you can fetch the current value, modify it locally, and send the full new object back via update_step with replace: ["<path>"]. Avoids the "patched one key, silently wiped the others" trap. Source resolution - source: "auto" (default) — returns the draft step if a draft exists, else live. Matches update_step's routing for live workflows. - source: "live" — always reads from the live pipeline, ignoring any draft. - source: "draft" — returns the draft step or 404 if no draft exists. Never creates a draft. The response includes the resolved source: "live" | "draft" so you know which one you got. Response shape { workflowId, stepId, source, step: <PipelineStep>, contextRefs: { inputPagesUsed: ["company_url", ...], // {{input.X}} references found in the step stepRefs: ["fetch", "analyze", ...] // {{steps.X.*}} references found in the step }, draft?: { // present when a draft snapshot exists exists: true, draftCreatedAt, liveUpdatedAt, stale: boolean, // live advanced past draft.createdAt modifiedStepIds: [...], // step IDs differing between draft and live modifiedFields: ["steps", "context", ...] // top-level keys differing } } contextRefs tells you which upstream fields the step depends on — useful when you're about to break a downstream chain by editing inputs. draft.stale === true means the live workflow has been touched since the draft was created. Promoting will land older values for fields the agent didn't touch in this draft. Recovery: discard_draft and re-apply, or get_draft to inspect what's pending.. It is categorised as a Read tool in the Agentled MCP Server, which means it retrieves data without modifying state.
Register the Agentled MCP server in PolicyLayer and add a rule for get_step: 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 Agentled. Nothing to install.
get_step 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_step 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_step. 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_step is provided by the Agentled MCP server (@agentled/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 119 Agentled tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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