"Building OpenAI Agents SDK Using Official Documentation" matching MCP servers:

Exposes the OpenAI Admin API as MCP tools, enabling management of organization members, projects, service accounts, API keys, rate limits, usage, costs, and audit logs through natural language.

A stateless MCP server for incident lab operations, exposing MCP and health endpoints, with conformance testing.

Data observability for AI agents. Query alerts, monitor freshness, investigate schema drift, and trace lineage across your data warehouse via 53 MCP tools.

A secure MCP proxy server with JWT authentication, SQL guardrails, rate limiting, and Kafka audit logging, enabling enterprise-grade access control for MCP clients like Cursor and Claude Desktop.

An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.

An MCP server implementation for accessing Google Analytics 4 (GA4) data, built using the Model Context Protocol TypeScript SDK.

The official AppSignal MCP Server

Enables AI agents to check real-time status, uptime, and incidents for 200+ popular APIs (Stripe, OpenAI, AWS, GitHub, Twilio, etc.) through read-only, no-authentication tools.

Enables tracing mcp-use tool callbacks and capturing tool errors and exceptions in Sentry via MCP middleware, without SDK changes or additional services.

C#/.NET patterns and helpers for MCP tools that unreliable agents cannot misuse: result envelopes, corrective errors, safe defaults, truncation transparency, audit hooks. Builds on the official C# SDK and ships a runnable sample server.

Enables AI agents to query and analyze Graylog logs through MCP tools for search, aggregation, stream listing, and saved queries, with optional OpenAI-powered natural language orchestration and audit logging.

MCP server that exposes WebSocket session management as tools, allowing AI agents to open persistent connections, send typed frames, drain buffered messages, and monitor per-frame Shannon entropy to detect schema divergence.

Model Context Protocol (MCP) server for debugging, analyzing, and diagnosing Kubernetes clusters directly from AI agents.

Web analytics and AI session replay for agents: visitors, breakdowns, funnels, goals, revenue attribution, and AI-found site issues. Connects to the hosted Flowsery MCP server.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

Unified observability gateway for AI agents — one MCP server for Prometheus, Loki, and any backend.

Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands

MCP server for Kibana / Elasticsearch — log search, aggregations, index discovery, and dashboard browsing. Hits Elasticsearch REST API directly for log queries; falls back to Kibana Console proxy when no direct ES URL is configured. Supports ApiKey auth (best for agents), Basic auth, and anonymous access. All 5 tools are read-only (readOnlyHint: true). Returns structured JSON (outputSchema).

MCP server that diagnoses Apache Spark job failures and optimizes performance using stack-trace analysis and LLM providers, supporting EMR and local sources.

Minimal MCP server that integrates with Graylog, enabling agents to search, analyze, and inspect log messages across streams, discover fields and streams, and query multiple Graylog instances.