job_update_context
Update the job context by merging new data. Existing keys are preserved unless explicitly overwritten. Use this to record progress, update assignment statuses, or store intermediate results.
This record as markdown: /tools/io-github-saloprj-dialogbrain/job-update-context.md
What job_update_context does on Dialogbrain
AI agents use job_update_context 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 |
|---|---|---|---|
job_id | integer | — | The ID of the job to update |
updates | object | Yes | Key-value pairs to merge into job context |
Parameters from the server's own tool schema.
Why job_update_context is rated Medium
This tool modifies existing job context data reversibly through updates and merges. It does not delete data permanently (Destructive), execute arbitrary code (Execute), involve financial transactions (Financial), or merely retrieve data (Read).
From the tool's definition Tool description explicitly states 'Update the job context by merging new data' and 'update assignment statuses', which are modification operations. The tool allows writing and overwriting data ('Existing keys are preserved unless explicitly overwritten').
Attacks that exploit this kind of access
The rule that runs job_update_context 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 job_update_context, this is the rule to start with:
job_update_context 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 job_update_context call is checked against it from then on.
Questions about job_update_context
Update the job context by merging new data. Existing keys are preserved unless explicitly overwritten. Use this to record progress, update assignment statuses, or store intermediate results. 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.
job_update_context accepts 2 parameters: job_id, updates. Required: updates. 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 job_update_context: 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.
job_update_context 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 job_update_context 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 job_update_context. 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.
job_update_context 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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