Gets an issue from the task trellis system Use this tool to retrieve detailed information about a specific issue by its unique ID. Returns the complete issue data including metadata, relationships, content, and activity history. Key information retrieved: - Issue metadata (type, title, status, pr...
AI agents call get_issue to retrieve information from Task Trellis MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs data retrieval only. It queries and returns issue information without creating, modifying, deleting, or executing any operations. The described functionality (retrieving metadata, relationships, content, and activity history) are all read operations with no side effects. The low severity reflects minimal risk from misuse—an AI agent retrieving task data poses no significant danger.
From the tool's definition Tool description states 'Gets an issue from the task trellis system' and 'Returns the complete issue data including metadata, relationships, content, and activity history'.
Documented attack patterns abuse exactly the kind of access get_issue gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Task Trellis MCP, and nothing reaches the server without passing your rules. This is the rule we recommend for get_issue:
{
"version": "1",
"default": "deny",
"tools": {
"get_issue": {}
}
}get_issue is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Gets an issue from the task trellis system Use this tool to retrieve detailed information about a specific issue by its unique ID. Returns the complete issue data including metadata, relationships, content, and activity history. Key information retrieved: - Issue metadata (type, title, status, priority, timestamps) - Hierarchical relationships (parent, children, prerequisites) - Content body and description - Activity log and change history - File associations and modifications - Current state and progress indicators Usage scenarios: - Review task details before starting work - Check issue status and dependencies - Examine change history and activity logs - Understand parent-child relationships - Verify prerequisite completion - Access associated file changes Essential for understanding the full context of a work item before making modifications or planning next steps. It is categorised as a Read tool in the Task Trellis MCP MCP Server, which means it retrieves data without modifying state.
Register the Task Trellis MCP server in PolicyLayer and add a rule for get_issue: 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 Task Trellis MCP. Nothing to install.
get_issue 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 get_issue 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 get_issue. 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.
get_issue is provided by the Task Trellis MCP server (langadventurellc/task-trellis-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Task Trellis MCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
16 Task Trellis MCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.