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kiro_pool_stats

Monitor process pool performance statistics to optimize response times and resource usage in Kiro CLI MCP Server workflows.

Instructions

Get process pool statistics for performance monitoring

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The main handler function for the kiro_pool_stats tool. It retrieves and returns the process pool statistics from the CommandExecutor instance.
    async def _handle_pool_stats(
        command_executor: CommandExecutor,
    ) -> dict[str, Any]:
        """Handle kiro_pool_stats tool call - get process pool statistics."""
        return {
            "pool_stats": command_executor.pool_stats,
        }
  • Tool schema definition including name, description, and empty input schema (no parameters required).
    {
        "name": "kiro_pool_stats",
        "description": "Get process pool statistics for performance monitoring",
        "inputSchema": {
            "type": "object",
            "properties": {}
        }
    },
  • Dispatch registration in the main handle_call_tool function that routes calls to the specific handler.
    elif name == "kiro_pool_stats":
        result = await _handle_pool_stats(command_executor)
  • Property in CommandExecutor that provides access to the process pool stats, delegating to ProcessPool.stats if pool is active.
    @property
    def pool_stats(self) -> dict[str, Any]:
        """Get process pool statistics."""
        if self._process_pool:
            return self._process_pool.stats
        return {"enabled": False}
  • Core statistics property in ProcessPool class that computes and returns detailed pool metrics including counts and hit rate.
    @property
    def stats(self) -> dict[str, int]:
        """Get pool statistics."""
        return {
            **self._stats,
            "idle_count": len(self._idle_processes),
            "active_count": len(self._active_processes),
            "hit_rate": (
                self._stats["cache_hits"] / 
                max(1, self._stats["cache_hits"] + self._stats["cache_misses"])
            ) * 100,
        }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'Get' operation (implying read-only) and mentions 'performance monitoring' purpose, but doesn't disclose important behavioral traits like whether this requires special permissions, what format the statistics are returned in, if there are rate limits, or if the data is real-time vs cached.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple read operation and front-loads the essential information ('Get process pool statistics').

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless read operation with no output schema, the description provides basic purpose but lacks important context. It doesn't explain what 'process pool statistics' includes, the return format, or whether this is for system-wide or specific pool monitoring. Given the complexity is low (no parameters) but no output schema exists, more detail about what information is returned would be helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage. The description appropriately doesn't discuss parameters since none exist. It focuses on what the tool retrieves ('process pool statistics') rather than parameter details, which is correct for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get process pool statistics for performance monitoring' with a specific verb ('Get') and resource ('process pool statistics'). It distinguishes from siblings by focusing on pool statistics rather than agents, sessions, tasks, or chat functions. However, it doesn't explicitly differentiate from all possible sibling tools in the list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. While 'for performance monitoring' gives some context, it doesn't specify prerequisites, timing considerations, or when to choose this over other monitoring-related tools (though none are explicitly listed in siblings).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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