Metrx

Showing 25 of 38 from the live probe.

• Get Cost Summary

  Get a comprehensive cost summary for your AI agent fleet. Returns total spend, call counts, error rates, agent breakdown, revenue attribution (if available), and optimization opportunities. Use this as the starting point for understanding your agent economics. Do NOT use for real-time per-request cost checking — use OpenTelemetry spans for that.

• List Agents

  List all AI agents in your organization with their status, category, and cost. Optionally filter by status or category. Returns agent IDs needed for other tools. Do NOT use for detailed per-agent analysis — use get_agent_detail for that.

• Get Agent Detail

  Get detailed information about a specific agent including its model, framework, category, outcome configuration, and failure risk score. Do NOT use for fleet-wide overviews — use get_cost_summary instead.

• Get Optimization Recommendations

  Get AI-powered cost optimization recommendations for a specific agent or your entire fleet. Returns actionable suggestions including model switching, token guardrails, provider arbitrage, batch processing opportunities, and revenue intelligence insights. Each suggestion includes estimated monthly savings and confidence level. Do NOT use for implementing fixes — use apply_optimization for one-click fixes or create_model_experiment to validate first.

• Apply Optimization

  Apply a one-click optimization recommendation to an agent. Only works for suggestions marked as "one_click: true". Common optimizations include setting max_tokens limits and switching models. Do NOT use for unvalidated changes — run create_model_experiment first if unsure about impact.

• Route Model Selection

  Get a model routing recommendation for a specific task based on complexity. Uses the agent's historical performance data and cost analysis to suggest the optimal model for each task complexity level. Helps reduce costs by routing simple tasks to cheaper models while keeping complex tasks on premium models. Do NOT use for comparing all models at once — use compare_models for static pricing.

• Compare Models

  Compare LLM model pricing and capabilities across providers. Returns pricing per 1M tokens, context window sizes, batch/cache support, and cost savings estimates for switching from a current model to alternatives. Works without any usage data (Day 0 value). Do NOT use for agent-specific recommendations — use get_optimization_recommendations which factors in actual usage patterns.

• Get Budget Status

  Get the current status of all budget configurations. Shows spending vs limits, warning/exceeded counts, and enforcement modes. Use this to monitor spending governance across your agent fleet. Do NOT use for creating/changing budgets — use set_budget or update_budget_mode.

• Set Budget

  Create or update a budget configuration for an agent or the entire organization. Budgets enforce spending limits with configurable enforcement modes: "alert_only" (notify but don't block), "soft_block" (block with override), or "hard_block" (strict enforcement). Specify limits in dollars. Do NOT use just to change enforcement mode — use update_budget_mode for that.

• Update Budget Mode

  Change the enforcement mode of an existing budget or pause/resume it. Use "alert_only" for monitoring, "soft_block" for overridable limits, or "hard_block" for strict enforcement. Do NOT use to create new budgets — use set_budget for that.

• Get Alerts

  Get active alerts and notifications for your agent fleet. Includes cost spikes, error rate increases, budget warnings, and system health notifications. Optionally filter by severity. Do NOT use for configuring alert triggers — use configure_alert_threshold for that.

• Acknowledge Alert

  Mark one or more alerts as read/acknowledged. This removes them from the unread alerts list but preserves them in history. Do NOT use for resolving the underlying issue — take action on the alert first.

• Get Failure Predictions

  Get predictive failure analysis for your agents. Shows upcoming risk of error rate breaches, latency degradation, cost overruns, rate limit risks, and budget exhaustion. Each prediction includes confidence level and recommended actions. Do NOT use for current/past failures — use get_alerts for active issues.

• Create Model Experiment

  Start an A/B test comparing two LLM models for a specific agent. Routes a percentage of traffic to the treatment model and tracks cost, latency, error rate, and quality metrics. The experiment runs until statistical significance is reached or the max duration expires. Do NOT use for one-off model comparisons — use compare_models for static pricing data.

• Get Experiment Results

  Get the current results of a model routing experiment. Shows sample counts, metric comparisons, statistical significance, and the current winner (if determined). Do NOT use for starting experiments — use create_model_experiment.

• Stop Experiment

  Stop a running model routing experiment. The experiment results are preserved. If the treatment model won, you can optionally promote it as the new default. Do NOT use for pausing experiments temporarily — stopping is permanent.

• Run Cost Leak Scan

  Run a comprehensive cost leak audit across your entire agent fleet. Identifies 7 types of cost inefficiencies: idle agents, model overprovisioning, missing caching, high error rates, context bloat, missing budgets, and cross-provider arbitrage opportunities (covers amazon, anthropic, cohere, deepseek, google, meta, mistral, openai, xai). Returns a scored report with fix recommendations and estimated monthly savings. Supports output_format="json" for machine-readable output in CI/CD pipelines. Do

• Attribute Task to Outcome

  Link an agent task/event to a business outcome for ROI tracking. This creates a mapping between agent actions and measurable business results. Do NOT use for reading attribution data — use get_attribution_report or get_task_roi.

• Get Agent ROI

  Calculate return on investment for an agent. Shows total costs (LLM API calls), total outcomes (attributed business value), ROI multiplier, and breakdown by model and outcome type. Useful for identifying which agents generate the most value per dollar spent. Do NOT use for fleet-wide ROI — use generate_roi_audit for that.

• Get Attribution Report

  Get attribution report showing which agent actions led to business outcomes. Shows outcome counts, total values, confidence scores, and top contributing agents. Do NOT use for board-level reporting — use generate_roi_audit for formal audit reports.

• Get Upgrade Justification

  Generate an ROI report explaining why paying for Metrx makes sense based on current usage. Analyzes usage and optimization potential, then recommends Verify (a buyable per-verdict proof — no verdict, no charge) and Platform (priced to your managed LLM spend; talk to us). Legacy Lite/Pro tiers are retired and never recommended.

• Configure Alert Threshold

  Set up cost or operational alert thresholds for a specific agent or org-wide. Alerts can trigger email notifications, webhooks, or automatically pause the agent. Use for real-time cost governance and operational safety. Thresholds run server-side automatically. Do NOT use for viewing current alerts — use get_alerts instead.

• Generate ROI Audit Report

  Generate a comprehensive ROI audit report for your AI agent fleet. Includes per-agent cost/revenue breakdown, attribution confidence scores, optimization opportunities, and risk flags. Suitable for board reporting and compliance. Do NOT use for quick per-agent ROI checks — use get_task_roi for individual agents.

• Get Upgrade Business Case

  Generate a personalized ROI business case for paying for Metrx. Uses real usage data to quantify optimization potential, then recommends Verify (a buyable per-verdict proof — no verdict, no charge) and Platform (priced to your managed LLM spend; talk to us). Ideal for agents that received a 402 and want a compelling proposal for their operator. Legacy Lite/Pro tiers are retired and never recommended. Returns structured data plus a report.

• Get Agent Health Scores

  Returns health score, grade, and data completeness for all agents in the organization. Health scores are composite 0-100 scores blending cost efficiency, ROI, error rate, quality drift, failure risk, and latency. Grades: A (80-100), B (60-79), C (40-59), D (20-39), F (0-19). Use this to get a fleet-wide health overview.