plugin.json

{
  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
  "name": "app-6aafceb525f48191a338b4bce5fc7888",
  "version": "1.0.2",
  "description": "Ask your AI cost, runway, and guardrails from your real AtlasBurn data. Read-only.",
  "author": {
    "name": "Akhil Anand",
    "email": "contact@atlasburn.com",
    "url": "https://atlasburn.com"
  },
  "homepage": "https://atlasburn.com",
  "keywords": [
    "ai-cost",
    "llm",
    "finops",
    "observability",
    "guardrails",
    "mcp"
  ],
  "extensions": {
    "com.openai": {
      "interface": {
        "displayName": "AtlasBurn",
        "shortDescription": "Track and control AI spend",
        "longDescription": "Connect AtlasBurn to see where your AI spend is going across models, providers, workflows, and agents. Ask ChatGPT to break down usage and costs, identify expensive requests or unusual spending patterns, investigate retries and costly agent behavior, and review forecasts and budget guardrails.\n\nAtlasBurn helps teams understand what is driving their AI costs and spot problems before they grow into larger bills.\n\nThe connection is read-only and content-blind: it reads cost and usage telemetry from your AtlasBurn workspace, never changes your settings, and never sees your prompts or model outputs.",
        "developerName": "Akhil Anand",
        "category": "Developer Tools",
        "capabilities": [
          "Show total AI spend, token usage, and request volume",
          "Break down AI costs by model and product feature",
          "Forecast monthly AI bill, daily burn, and budget runway",
          "Report guardrail status, budgets, and recent breaches",
          "Flag retry loops and runaway agent behavior",
          "Read-only: never changes settings or sees prompts and outputs"
        ],
        "websiteURL": "https://atlasburn.com",
        "supportURL": "https://docs.atlasburn.com/contact",
        "privacyPolicyURL": "https://atlasburn.com/privacy/",
        "termsOfServiceURL": "https://atlasburn.com/terms/",
        "defaultPrompt": [
          "How much have we spent on AI in the last 30 days?",
          "Which models and features are driving our AI costs?",
          "Check our runway, guardrail status, and whether any agent looks like it's looping."
        ],
        "brandColor": "#5273FF",
        "brandColorDark": "#5273FF",
        "composerIcon": "./assets/icon.png",
        "logo": "./assets/logo.png",
        "logoDark": "./assets/logo-dark.png"
      },
      "review": {
        "test_cases": {
          "positive": [
            {
              "description": "Total AI spend and usage for the last 30 days",
              "prompt": "How much have we spent on AI in the last 30 days?",
              "tools_triggered": "get_cost_summary",
              "expected_behavior": "Calls get_cost_summary and reports the demo workspace's total cost in USD, prompt and completion tokens, and request count for the 30-day window, stating the window it used."
            },
            {
              "description": "Cost attribution by model and feature",
              "prompt": "Which models and product features are driving our AI costs this month?",
              "tools_triggered": "get_cost_breakdown",
              "expected_behavior": "Calls get_cost_breakdown and lists spend per model and per feature, ranked from most to least expensive, naming the largest cost driver."
            },
            {
              "description": "Monthly bill and budget runway forecast",
              "prompt": "What's our projected monthly AI bill, and how many days of budget runway do we have?",
              "tools_triggered": "get_runway_forecast",
              "expected_behavior": "Calls get_runway_forecast and reports the projected monthly bill and current daily burn. If capital reserves are not configured, it says the runway forecast is not ready and names the missing inputs instead of inventing a number."
            },
            {
              "description": "Guardrail state and recent breaches",
              "prompt": "Are any of our AI budget guardrails tripped right now?",
              "tools_triggered": "get_guardrail_status",
              "expected_behavior": "Calls get_guardrail_status and reports whether guardrails are enabled, the mode, the enforcement status (ACTIVE, THROTTLED, or SUSPENDED), the configured daily and hourly budgets, and any recent breach events."
            },
            {
              "description": "Runaway or looping agent detection",
              "prompt": "Does anything in our AI traffic look like an agent stuck in a retry loop?",
              "tools_triggered": "get_runtime_signals",
              "expected_behavior": "Calls get_runtime_signals and summarizes request rate, retry rate, loop probability, context-expansion rate, and model mix, then says plainly whether the signals look like a runaway or looping agent."
            }
          ],
          "negative": [
            {
              "description": "General model pricing question unrelated to the user's own data; AtlasBurn tools should not be called.",
              "prompt": "What does GPT-4o cost per million tokens?"
            },
            {
              "description": "Request to change settings; AtlasBurn is read-only, so it should not attempt a change and should point the user to the AtlasBurn dashboard.",
              "prompt": "Raise my AtlasBurn daily budget to $500 and turn off the guardrails."
            },
            {
              "description": "Non-AI cloud spend that AtlasBurn does not track; AtlasBurn tools should not be called.",
              "prompt": "How much did we spend on AWS EC2 instances last month?"
            }
          ]
        },
        "demo_recording_url": "https://drive.google.com/file/d/1tS66ZKYmyLOhchxDwEkbcBkean1ikAQa/view?usp=sharing",
        "commerce": false
      },
      "publication": {
        "release_notes": "Initial release. AtlasBurn lets ChatGPT read your AI cost and economics data: total spend and token usage, cost broken down by model and feature, a runway and burn forecast, guardrail status with recent breaches, and runtime signals that flag runaway or looping agents. Read-only and content-blind: it never accesses your prompts or model outputs."
      },
      "apps": "./.app.json"
    }
  }
}