post_insight_results
Manage cloud insights representing recommendations and findings for cloud resources. Creates or updates multiple insights in a single batch request. Each insight in the batch includes its metadata and resource results inline. For granular control over insight metadata and resource results indepen...
This record as markdown: /tools/doit/post-insight-results.md
What post_insight_results does on Doit
AI agents use post_insight_results to create or update resources in Doit, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Doit environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
results | array | Yes | List of insights to create or update. |
customerContext | string | — | Scope the request to a specific customer by ID. Required for DoiT employees (whose token isn't tied to a single customer); omit for direct customer users. |
Parameters from the server's own tool schema.
Why post_insight_results is rated Medium
An AI agent can call post_insight_results faster than any human can review: one bad instruction and it creates or modifies resources in Doit by the hundred, each call as confident as the last.
Risk signalsHigh parameter count (30 properties)
Attacks that exploit this kind of access
The rule that runs post_insight_results safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Doit, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For post_insight_results, this is the rule to start with:
post_insight_results 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 Doit, apply this rule, and every post_insight_results call is checked against it from then on.
Questions about post_insight_results
Manage cloud insights representing recommendations and findings for cloud resources. Creates or updates multiple insights in a single batch request. Each insight in the batch includes its metadata and resource results inline. For granular control over insight metadata and resource results independently, use the single-insight and resource-results endpoints instead. It is categorised as a Write tool in the Doit MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
post_insight_results accepts 2 parameters: results, customerContext. Required: results. The full parameter table on this page comes from the server's own tool schema.
Register the Doit MCP server in PolicyLayer and add a rule for post_insight_results: 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 Doit. Nothing to install.
post_insight_results 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 post_insight_results 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 post_insight_results. 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.
post_insight_results is provided by the Doit MCP server (@doitintl/doit-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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