job_read_context
Read the current job context. Returns the full state of your active job including assignments, escalations, and any data you previously stored.
This record as markdown: /tools/io-github-saloprj-dialogbrain/job-read-context.md
What job_read_context does on Dialogbrain
AI agents call job_read_context to retrieve information from Dialogbrain without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
job_id | integer | — | The ID of the job to read |
Parameters from the server's own tool schema.
Why job_read_context is rated Low
This is a read operation that queries and retrieves job state information (assignments, escalations, stored data). It has no capability to modify, delete, or execute external operations.
From the tool's definition Tool name contains 'read' and description states 'Read the current job context' and 'Returns the full state' — purely retrieves data with no side effects.
Attacks that exploit this kind of access
The rule that runs job_read_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_read_context, this is the rule to start with:
job_read_context is read-only, so it stays allowed. 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_read_context call is checked against it from then on.
Questions about job_read_context
Read the current job context. Returns the full state of your active job including assignments, escalations, and any data you previously stored. It is categorised as a Read tool in the Dialogbrain MCP Server, which means it retrieves data without modifying state.
job_read_context accepts 1 parameter: job_id. 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_read_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_read_context 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 job_read_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_read_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_read_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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