Get status of parallel implementation sessions
AI agents call get_parallel_status to retrieve information from Agent Collaboration MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool is purely informational—it observes the state of parallel implementations and returns status data. There are no side effects, no code execution, no data modification, and no irreversible actions. It is a straightforward read operation that an AI agent would use to monitor progress, making it low severity with high confidence.
From the tool's definition Tool name 'get_parallel_status' and description 'Get status of parallel implementation sessions' indicate a query/monitoring operation that retrieves information about running sessions without modifying, executing, or deleting anything.
Documented attack patterns abuse exactly the kind of access get_parallel_status gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Agent Collaboration MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_parallel_status:
{
"version": "1",
"default": "deny",
"tools": {
"get_parallel_status": {}
}
}get_parallel_status is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get status of parallel implementation sessions. It is categorised as a Read tool in the Agent Collaboration MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Agent Collaboration MCP Server MCP server in PolicyLayer and add a rule for get_parallel_status: 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 Agent Collaboration MCP Server. Nothing to install.
get_parallel_status 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_parallel_status 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_parallel_status. 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_parallel_status is provided by the Agent Collaboration MCP Server MCP server (nishimoto265/agent_collaboration_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Agent Collaboration MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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6 Agent Collaboration MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.