"Understanding or Using Memory Lists" matching MCP connectors:

Diagnose AI agent failures & translate ambiguous human input into clear intent using RPCS-1.

Pay-per-call URL intelligence for AI agents via x402 USDC on Base; no account or API key.

Pay-per-call live telemetry, environmental metrics, transit state vectors, blockchain reads, network/security lookups and real-time utilities for AI agents using x402 and MCP.

Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.

Connect engineering metrics, DORA performance, and deploy risk scoring to any AI assistant. Score PRs for deployment risk using a 36-signal model, query team health, incidents, coverage, and more.

Uptime, SSL, DNS and domain monitoring you can talk to from Claude or any MCP client.

Privacy-first web analytics, exposed to your AI agent as a first-class data source. The agent sees your traffic, referrers, geos, devices, live visitors, and custom events, and can reason across them. Ask what changed since the last deploy, why a campaign underperformed, which segment of signups actually activated, or have it build a conversion funnel and alert you when bounce rate spikes.

Read-only MCP server for Yandex Metrika analytics. Query visits, sources, geography, devices and more in plain language — directly in Claude, Cursor, or any MCP client.

Mobile observability for AI agents. Investigate crashes, hangs, ANRs, bugs, and app performance, and triage app store reviews, directly from your IDE or terminal.

Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.

Track agent ROI and enrich companies via MCP. Remote, OAuth 2.1, no install or keys.

Debug production issues using Shipbook logs and Loglytics error insights.

Access and analyze real-time geographic sensor data across various global locations. Identify specific data sources and monitor environmental or behavioral attributes through structured queries. Gain instant visibility into distributed physical assets and their performance metrics.

The Polar Signals MCP server enables AI assistants to connect directly with performance profiling data, allowing users to analyze application performance through natural language queries. Key capabilities include querying CPU performance and memory usage, exploring profiling metadata like profile types and labels, and providing AI-driven code optimization suggestions directly within development environments like Claude Code or Cursor.

The Buildkite MCP server exposes Buildkite product data (pipelines, builds, jobs, and test data) to AI tools, editors, and agents through the Model Context Protocol. It provides capabilities including pipeline creation and management, build monitoring with specialized tools like 'wait_for_build', efficient log querying using Apache Parquet conversion and caching, and OAuth-based authentication for both read-write and read-only access to Buildkite's REST API.