This record as markdown: /tools/aiwerk-mcp-server-ghl/blogs-update-blog-post.md
What blogs_update_blog_post does on Ghl
AI agents use blogs_update_blog_post to create or update resources in Ghl, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ghl environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
tags | array | — | |
title | string | Yes | |
author | string | Yes | This needs to be author id, which you can get from the author get api call. |
blogId | string | Yes | You can find the blog id from blog site dashboard link |
postId | string | Yes | Path parameter postId. |
status | string | Yes | |
rawHTML | string | Yes | |
urlSlug | string | Yes | |
imageUrl | string | Yes | |
categories | array | Yes | This needs to be array of category ids, which you can get from the category get api call. |
locationId | string | — | Defaults to GHL_LOCATION_ID when omitted. |
description | string | Yes |
Parameters from the server's own tool schema.
Why blogs_update_blog_post is rated Medium
An AI agent can call blogs_update_blog_post faster than any human can review: one bad instruction and it creates or modifies resources in Ghl by the hundred, each call as confident as the last.
Risk signalsHigh parameter count (15 properties)
Attacks that exploit this kind of access
The rule that runs blogs_update_blog_post safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ghl, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For blogs_update_blog_post, this is the rule to start with:
blogs_update_blog_post 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 Ghl, apply this rule, and every blogs_update_blog_post call is checked against it from then on.
Questions about blogs_update_blog_post
Update Blog Post. It is categorised as a Write tool in the Ghl MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
blogs_update_blog_post accepts 12 parameters: tags, title, author, blogId, postId, status, rawHTML, urlSlug, imageUrl, categories, locationId, description. Required: title, author, blogId, postId, status, rawHTML, urlSlug, imageUrl, categories, description. The full parameter table on this page comes from the server's own tool schema.
Register the Ghl MCP server in PolicyLayer and add a rule for blogs_update_blog_post: 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 Ghl. Nothing to install.
blogs_update_blog_post 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 blogs_update_blog_post 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 blogs_update_blog_post. 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.
blogs_update_blog_post is provided by the Ghl MCP server (@aiwerk/mcp-server-ghl). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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