"A clean web agent named x402-cleanweb-agent" matching MCP servers:

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Exposes rate-limit and usage data for AI coding agent CLIs as MCP tools, enabling querying of usage snapshots and reset times.

An MCP server that provides append-only, tamper-evident local receipts for AI agent actions, capturing command executions, outputs, and handoff evidence.

Local-first observability for AI agent stacks. Provides MCP tools for agents to check health, shadow configs, and burnout metrics proactively.

Registers a set of inert, tempting decoy tools with any MCP-capable client so that any call from a hijacked or prompt-injected agent instantly raises an alert with full attack context and a per-call trace token. Also plants worthless canary tokens in honeypot files that trigger zero-false-positive leak alerts when they surface in agent output, files, or outbound requests.

Tracks AI agent token usage and spending in real time, with budget alerts, per-task cost breakdown, and a visual dashboard.

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.

Enables recording and querying MCP tool call traces, including latency, failures, and token costs, per agent run.

Analyzes AI agent traces to diagnose failures, score performance, and provide actionable fixes using tools like judge_trace and trace_breakdown.

Enables AI agents to explore a synthetic product-event dataset and answer questions about signups, trends, funnels, and property breakdowns using a small set of composable, self-describing tools that return precomputed totals and actionable error hints.

MCP-based production monitoring agent for manufacturing OEE, providing real-time monitoring, downtime root-cause analysis, and automated alerts through integration with SQL Server, MES APIs, and incident management systems.

Enables AI agents to self-report every tool call, token usage, decision, and session grade into a local SQLite store, so users can trace execution, audit reasoning, and see per-session costs in a local dashboard. Also supports proxy mode to intercept and trace MCP tool invocations for agents that cannot self-report.

MCP server for tracking and managing costs per AI agent, with budget alerts, anomaly detection, and model cost comparison.

Enables users to retrieve Agent Failure-Recovery SLA terms, check agent compliance and credit history, and run adversarial chaos tests against agent infrastructure.

MCP server that analyzes AI agent execution logs to calculate reliability scores, detect failure patterns, and suggest concrete improvements for making AI agents more reliable.

Enables natural language queries for AWS CloudWatch logs, metrics, and alarms via an LLM agent with MCP tools.

Local-first MCP server for credit risk analysis. It enables explainable rule discovery, statistical validation, PSI drift monitoring, and root-cause diagnosis on user-approved Parquet data while preserving privacy.

Enables AI apps to inspect and manage a NextDNS account in plain language — profiles, analytics, query logs, blocklists, security and parental control settings — with every change saved and reversible via undo. It runs self-hosted on your own computer or server, keeps per-profile read/write/hidden permissions, and requires double confirmation for deletions or overwrites.

Provides cost analysis of AI agent usage by previewing scrubbed metadata before upload and analyzing spend, cache-hit rates, and waste recovery through a local or remote server.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.