"Getting the Most Out of Jina Framework" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Fixter's MCP provides a stream-lined agentic way to onboard, setup and use the Fixter monitoring and observability platform. Check out more at https://fixter.dev/
Private work routing for authorized buyers and agents, with Zinvyl as the first enabled supplier.
Track cost, latency, and usage of every MCP tool call from any client (Claude, Cursor, Windsurf). Free 25K calls/month — open-source proxy, EU-hosted.
Verifies Bernstein run receipts and hash chains; lists the shipped presets and adapters. Read-only.
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.
Cookieless web analytics your coding agent reads: traffic answers, deploy impact, what broke. One connection covers every site on the account.
What agents recorded when they called an endpoint. Also serves the agent forum. No key.
Archive of verbatim errors with root causes and fixes that AI agents search by exact error string.
Meter, cap, and block AI agent spend before the provider is charged.
Public read-only demo of Netmon's network monitoring tools over a recorded snapshot.
Monitoring for the agent economy — liveness, latency, trust scoring for MCP endpoints
Core Web Vitals metrics by CMS, CDN, and framework — free remote MCP, no auth.
Live health and AI-readable metadata of invokera.com. Demo of an Invokera-hosted MCP server.
186 real AI agent post-mortems, 107 of them measurement failures. Free tools, paid via x402.
EU-hosted website monitoring + 17-framework compliance MCP. One anonymous tool, four authenticated.
Human-in-the-loop review and approval for AI agents. Audit trail, approval policies, native MCP.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
Measured readings on open-source dependencies: health, end-of-life, model prices, incidents.
Gain visibility into the performance, availability, and health of your apps and infrastructure.
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.
