"Automated Browser Tools or Software" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Free-trial AI fleet spend meter. Quote agent fleet spend from token usage. 100 calls or 14 days. No auth. Official MCP Registry: io.github.mtardy90-sudo/bootlace-ai-spendmap.
Is GitHub, npm, Cloudflare or your AI provider down right now? 20 status pages, one call.
Four x402 tools: endpoint checks, extraction, evidence verification, domain intelligence.
MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.
Agent cost, latency and quality analysis, report delivery and order status. Crypto payments paused.
Read-only MCP for AI usage profiles, leaderboards, stats, and docs; no writes or private data.
Search + patterns, maturity assessment, context pricing, redaction checks - a practice as tools.
Public read-only demo of Netmon's network monitoring tools over a recorded snapshot.
Vouch — independently measured reliability scores for MCP tools, not self-reported claims.
Observatory operated and funded by devlo: real tools on frozen tasks; intervals, cost, limits.
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Discover Frontier inference capabilities and read sanitized usage through read-only tools.
Monitoring for agencies — uptime, SSL, DNS, blocklists, AI visibility, MCP health. 8 no-auth tools.
CVE intelligence: exploitation (KEV/EPSS), detection coverage, fixed versions. All tools keyless.
Live status for 172 cloud and SaaS vendors from their official feeds. Is it you, or is it them?
Live reliability for AI agent tools: is it working right now, and how do I call it correctly?
186 real AI agent post-mortems, 107 of them measurement failures. Free tools, paid via x402.
Diagnose AI workflows for failure, security, and handoff risks — RED/AMBER/GREEN per node.
The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.
The Cortex MCP server provides read-only access to real-time engineering context from the Cortex developer portal, allowing AI coding assistants to answer natural language questions about your organization's catalog (microservices, libraries, domains, teams, infrastructure), scorecards (engineering standards and best practices), initiatives (goals and deadlines), and Engineering Intelligence metrics. It includes tools for querying documentation, tracking personal entities, and accessing AI-assisted insights across the entire Cortex ecosystem.
