"Exploring RAG through defined workspaces" matching MCP connectors:

Run a prompt through a LangChain (system + human) chain over Gemini on Vertex AI; optional LangSmith

Read AI-gateway analytics, configs, virtual keys, workspaces and users; log request feedback.

Trace, evaluate, and optimize your LLM, RAG, and agent apps with Noveum observability.

Company analytics for Deepgent workspaces: usage, conversations, quality, billing, KB status.

Let AI agents monitor and manage your infrastructure through the Model Context Protocol. Query, create, and resolve — all in natural language.

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.

Access New Relic observability data through MCP - query metrics, logs, traces, entities, and more