"How to view GitHub repository history" matching MCP servers:

Enables LLMs to read and analyze Spark History Server data, including jobs, stages, executors, and SQL executions, to diagnose failures and optimize performance.

Exposes Spark History Server data as tools for AI agents, enabling natural language querying of Spark applications, jobs, stages, and performance metrics.

Connect AI agents like Claude, Cursor, and Gemini to historical Windows performance and privacy data gathered by the AppControl Windows app. This server enables natural language queries into CPU, GPU, and temperature history alongside system security events for deep local auditing.

Exposes Spark History Server metrics and metadata as tools for LLM-based analysis of Spark applications. It enables deep optimization of Spark jobs by providing access to job summaries, stage details, SQL execution plans, and executor performance.

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

A MCP server for tracking AI usage metrics and structured logs across applications. Monitor model calls, analyze usage patterns, track costs, and debug AI interactions.

Real-time GitHub Actions observability through VS Code with GitHub Copilot, providing DevOps metrics and interactive dashboards.

Enables multi-agent system auditing with seven-layer analysis, cost governance, compliance mapping, and watchdog monitoring. Offers tools for audit execution, cost analysis, report verification, regulation lookup, remediation suggestions, and fix tracking.

MCP server for monitoring health of MCP servers, GitHub Actions, GitLab pipelines, and HTTP endpoints with health history, TLS expiry, assertions, diagnostics, and alerts.

Enables AI agents to run container forensics, generate container obituaries, and audit git repository history as native MCP tools, producing markdown reports they can reason over.

Enables AI assistants to interact with Healthchecks.io, allowing users to list, create, update, pause, resume, and delete checks, as well as view ping history and integrations.

Enforces delegated permissions and Cedar authorization before each tool call, keeps upstream credentials server-side, blocks external writes after private data exposure, and records durable audit history so agents can safely use approved repository tools.

Enables coding agents to debug backend requests by providing tools to list, view, wait for, diagnose, and clear request traces.

Gives an LLM agent a fixed, typed set of on-call tools over a self-hosted GitOps Kubernetes platform, reading metrics, logs, alerts and deploy history from Prometheus, Loki, Alertmanager and GitHub without any cluster write access. Rollbacks and config changes are proposed as one-shot ids that require explicit human confirmation before becoming pull requests against the Git repository, with both steps recorded in an audit log.

Analyzes how search engines and AI systems discover, crawl, cite, and convert traffic via CLI and MCP tools.

MCP server for analyzing Ascend PyTorch Profiler performance data. Enables users to identify performance bottlenecks, analyze operator time, view communication overhead, and query trace data via natural language.

MCP server to access the Zabbix API, providing tools to list hosts, problems, triggers, items, history, and events.

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).

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.

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.

Compares AI coding tool spend before and after a migration -- Cursor, Claude Code, GitHub Copilot, OpenCode, Codex CLI -- via an MCP server, real numbers from each vendor's own API or local logs.

Enables analysis of JVM thread dumps to detect deadlocks, CPU spikes, pool exhaustion, and virtual thread pinning, returning a health score and interactive report link.

Provides a local MCP interface to query captured, PII-scrubbed prompt telemetry from coding agents and browser chats, enabling time summaries, prompt search, and activity analysis without data leaving the machine.

Enables agents to interact with the Logflare API by writing JavaScript functions that run in a Vercel Sandbox, using three tools: search, execute_read, and execute_write.