"Resources to Become an Expert in LangChain" matching MCP servers:

Enables read-only inspection and diagnosis of Windows, macOS, and Linux machines through eight diagnostic tools covering system profiles, baselines, disk health, startup audits, driver context, and upgrade planning, with voice summaries and a web simulator. It enforces an R0-R3 risk model in code so only safe R0 evidence collection is automatic while riskier actions remain manual guidance.

Enables AI agents to debug and repair historical Solaris/SPARC systems by providing an out-of-band control plane with guest DTrace, QEMU monitor access, SPARC-aware GDB, host eBPF/perf tracing, and an immutable evidence ledger for cross-layer hypothesis testing.

Enables language models to access LangSmith observability platform features including fetching conversation history, managing prompts, retrieving traces and runs, working with datasets and examples, and analyzing experiments.

Self-hosted Nightingale (n9e) includes a native Streamable HTTP MCP server at /mcp for querying alerts, rules, metrics, logs, hosts and dashboards with the caller's existing permissions. Users deploy the Nightingale server themselves; the implementation and deployment documentation are in the main repository, and the official website is https://n9e.io.

Enables tracing every tool call from a ChatGPT app into LangSmith, one run per tool call, grouped per conversation thread. Provides thread-level evaluation and monitoring of tool interactions.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

An MCP server for interacting with Prefect resources, enabling AI assistants to monitor, manage, and debug workflows.

Expert system hardware probe and performance diagnostic engine for AI, Gaming, and High-Performance workflows. Provides deep system insights such as real-time monitoring, thermal diagnostics, and LLM optimization.

Enables local LLM clients to capture and analyze network traffic using Wireshark's CLI tools, providing packet summaries, protocol hierarchies, conversations, endpoints, expert info, and stream reassembly without shell access.

Enables Pi to connect to Sentry's MCP endpoint, allowing an agent to search issues, events, and traces after OAuth sign-in.

Enables AI assistants to monitor and analyze industrial process data from DCS/SCADA systems via MCP protocol, with domain expert knowledge for anomaly detection and recommendations.

Enables AI assistants to query and analyze data in Azure Data Explorer, Log Analytics, and Microsoft Sentinel using Kusto Query Language (KQL) through tools, resources, and prompts.

A Model Context Protocol (MCP) server that provides safe, read-only access to Kubernetes resources for debugging and inspection. Built with security in mind, it offers comprehensive cluster visibility without modification capabilities.

Enables coding agents to coordinate profilers, benchmark tools, trace processors, and direct local targets so that explicit artifacts or commands become bounded runtime evidence with recorded provenance, coverage, and limitations. Its analysis and capture tools require no workspace setup, and durable evidence preservation remains optional.

Exposes VMware vROps resources, metrics, and alerts as MCP tools, allowing an LLM client to directly query monitored infrastructure.

An MCP server for querying Grafana Loki directly with a discovery-first workflow — labels, values, series, and LogQL queries without requiring Grafana.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

Provides cryptographically signed, tamper-evident compute usage receipts via a Model Context Protocol server, exposing tools to record usage, verify receipts, list the ledger, and verify ledger integrity without requiring any account, API key, or single cloud provider.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

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.

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).

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

Tells an agent what went wrong with its own Ollama calls, in sentences with the fix: lost prompt cache and where the prompt stopped matching, context overflow, a model running partly on CPU, tool schemas crowding the prompt. Also the last turn in numbers and which local models fit in video memory. Local, read-only, nothing leaves the machine.

Provides Claude with read-only access to PC diagnostics including hardware specs, live performance metrics, disk usage, network checks, and more, enabling natural language queries about PC health and performance.

MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.

Production-grade MCP Gateway in Go featuring dynamic OpenAPI tool registration, RBAC governance, in-flight PII/DLP redaction, and structured JSON audit logging.