Skip to main content
Glama

ai_status

Monitor and verify AI orchestration system health, model configurations, and capability test outcomes to ensure system readiness and aid in debugging processes.

Instructions

Health monitoring - check AI orchestration system status, model configuration, and capability testing results. Useful for debugging or verifying system readiness.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function that executes the ai_status tool logic. It checks AI availability, tests capabilities, gathers configuration info, and returns a formatted status response.
    export async function handleAIStatus(aiOrchestrator: AIOrchestrator) {
      try {
        const isAvailable = aiOrchestrator.isAIAvailable();
        const capabilities = await aiOrchestrator.testAICapabilities();
    
        const status = {
          available: isAvailable,
          capabilities,
          apiKeyConfigured: !!process.env.OPENROUTER_API_KEY,
          model: process.env.OPENROUTER_DEFAULT_MODEL || 'anthropic/claude-3.5-sonnet',
          configurationNote: 'API keys should be configured in MCP client settings under "env" section',
        };
    
        return {
          content: [
            {
              type: 'text',
              text: `AI Orchestration Status:\n\n${JSON.stringify(status, null, 2)}`,
            },
          ],
        };
      } catch (error) {
        logger.error('Failed to get AI status:', error as Error);
        return {
          content: [
            {
              type: 'text',
              text: `Error getting AI status: ${error instanceof Error ? error.message : String(error)}`,
            },
          ],
        };
      }
    }
  • Defines the tool name, description, and input schema (empty object) for ai_status, used in the ListTools response.
    {
      name: 'ai_status',
      description: 'Health monitoring - check AI orchestration system status, model configuration, and capability testing results. Useful for debugging or verifying system readiness.',
      inputSchema: {
        type: 'object',
        properties: {},
        additionalProperties: false,
        $schema: 'http://json-schema.org/draft-07/schema#',
      },
    },
  • Dispatches 'ai_status' tool calls to the handleAIStatus handler function.
    case 'ai_status':
      return handleAIStatus(aiOrchestrator);
  • Imports the handleAIStatus function for use in tool dispatching.
    import {
      handleGetInfo,
      handleAIProcess,
      handleAIStatus
    } from './orchestrator.js';
Behavior3/5

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

With no annotations provided, the description carries full burden. It indicates this is a diagnostic/read-only operation ('check', 'monitoring', 'debugging', 'verifying') which implies non-destructive behavior, but doesn't explicitly state permission requirements, rate limits, or detailed response format. It provides basic behavioral context but lacks comprehensive disclosure.

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 perfectly concise: two sentences with zero wasted words. The first sentence states purpose with specific components, the second provides usage context. Every element earns its place and information is front-loaded effectively.

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 diagnostic tool with no parameters, no annotations, and no output schema, the description provides adequate purpose and usage context. However, it doesn't describe what the output contains (status indicators, configuration details, test results format) or potential error conditions, leaving gaps in completeness for a tool that presumably returns system information.

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, maintaining focus on the tool's purpose and usage context. This meets the baseline expectation for parameterless tools.

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 health monitoring with specific components: checking AI orchestration system status, model configuration, and capability testing results. It distinguishes from siblings by focusing on system diagnostics rather than processing (ai_process) or general information retrieval (get_info), though it doesn't explicitly name those alternatives.

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

Usage Guidelines4/5

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

The description provides clear context for usage: 'Useful for debugging or verifying system readiness.' This gives practical guidance on when to use the tool, though it doesn't explicitly state when not to use it or name specific alternatives among siblings.

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Phoenixrr2113/Orchestrator-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server