linkedin_update_profile
Update the authenticated user's own LinkedIn profile. Supports adding/editing experience entries (role, company, skills, dates). Also supports updating location. Headline, summary, education are NOT supported by the API.
This record as markdown: /tools/io-github-saloprj-dialogbrain/linkedin-update-profile.md
What linkedin_update_profile does on Dialogbrain
AI agents use linkedin_update_profile to create or update resources in Dialogbrain, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dialogbrain environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
location | object | — | Location to set on profile (requires LinkedIn location ID) |
experience | object | — | Add or edit a professional experience entry |
Parameters from the server's own tool schema.
Why linkedin_update_profile is rated Medium
This tool modifies user profile data (experience entries, location, skills) on LinkedIn. These changes are reversible (can be edited or deleted later), so it falls under Write rather than Destructive. The severity is medium because misuse could damage professional reputation or create false work history, but the blast radius is limited to the authenticated user's own profile.
From the tool's definition Tool name is 'linkedin_update_profile' and description states 'Update the authenticated user's own LinkedIn profile' and 'adding/editing experience entries' — these are clearly write operations that modify user data reversibly.
Risk signalsHigh parameter count (18 properties)
Attacks that exploit this kind of access
The rule that runs linkedin_update_profile safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For linkedin_update_profile, this is the rule to start with:
linkedin_update_profile 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 Dialogbrain, apply this rule, and every linkedin_update_profile call is checked against it from then on.
Questions about linkedin_update_profile
Update the authenticated user's own LinkedIn profile. Supports adding/editing experience entries (role, company, skills, dates). Also supports updating location. Headline, summary, education are NOT supported by the API. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
linkedin_update_profile accepts 2 parameters: location, experience. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for linkedin_update_profile: 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 Dialogbrain. Nothing to install.
linkedin_update_profile 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 linkedin_update_profile 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 linkedin_update_profile. 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.
linkedin_update_profile is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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