"Finding people on GitHub" matching MCP servers:

MCP server for On-Call Health burnout analysis. Connects AI assistants to your on-call data for workload insights.

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

Provides structured MCP tools for local runtime observability and debugging of React Native/Expo apps on Android, enabling evidence-first diagnostics such as screenshots, UI trees, per-request network capture, performance metrics, and CDP-based console/heap/profiling exports.

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 exposing rlg (RustLogs) log files as tools for on-call / SRE agent workflows. Three tools over JSON-RPC stdio — tail_log (last N events, filtered by level/target), filter_log (predicate filtering), summarize_errors (grouped error taxonomy). Pure Rust. MIT OR Apache-2.0.

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.

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 log analysis for Tenable Patch Management (SaaS and on-prem) by parsing, de-duplicating, and decoding server/client logs into structured verdicts, ranked issues, and known-issue fixes.

Enables an LLM agent to investigate a Kong Gateway through fourteen read-only, schema-validated tools and a deterministic diagnostic engine, so it can trace routes, services, plugins and upstream health and explain faults such as why authentication is failing on a path. Every fact comes from the gateway itself, and the model only selects tools and writes the evidence-backed explanation.

Enables coding agents to read performance findings from sixty, get detailed evidence for each finding, and close or resolve them with descriptions, while also checking service telemetry and providing installation instructions.

Enables interaction with Jira Service Management Operations alerts and on-call schedules, including searching alerts, reading notes and activity logs, acknowledging, closing, annotating, adding responders, and querying current and next on-call rotations.

Exposes live industrial IoT telemetry to any MCP client, streaming simulated sensor data from a fleet of machines and detecting anomalies, with the ability to inject faults on demand.

Enables agents to query GPU availability across SSH hosts, find free GPUs, list hosts, and inspect fleet status using only nvidia-smi on the remote side.

Enables AI agents to interact with Freeplay, an ops platform for AI engineering teams, to analyze production logs, identify quality issues, iterate on prompts and agents using real data, and run experiments before deploying.

Read-only AMD GPU monitoring for Claude on Linux — VRAM, temperature, clocks, power, and running ML processes via rocm-smi.

MCP server exposing qbit_manage's Web API as tools, so an LLM can read and manage a qbit_manage instance: run maintenance commands on qBittorrent torrents, manage config files, scheduler, logs, security settings, and system state.

CI health reports for GitHub Actions: flaky tests named from logs, wasted compute priced in dollars, zombie crons, PR feedback time, plus 21 lint rules with safe auto-fixes. Read-only MCP tools wrap the same Go binary.

Enables AI agents to raise and resolve incidents in ITOC360, triggering escalation policies that page on-call responders and closing alerts when problems clear.

Enables read-only debugging of Azure applications from GitHub Copilot, VS Code, and other MCP clients, with tools for resource health, changes, metrics, Application Insights telemetry, and logs for App Service, Container Apps, and AKS.

An on-call agent for data incidents, built on a bio-inspired context protocol. It triages data quality and freshness issues using DataHub, computing severity, blast radius, and ownership from the catalogue before generating prose.

An MCP server that provides monitoring tools for Codex agents, allowing them to create and wait on conditions using commands, log files, file events, and custom probes, replacing polling loops with event-driven blocking.

An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.

Enables agents to check whether an execution trace contains the evidence needed for post-hoc causal reconstruction, retrieve individual requirement details, and list evidence requirements per failure class over MCP stdio. It works on files, directories, or inline JSON locally and never transmits or persists data.

The logs of everything on one machine — app, build, GPU, containers, serial board, OS — interleaved in the order they happened, and readable by an agent. Read-only: it can tail, search and wait, and it cannot write, delete or configure anything.

A read-only server that enables AI agents to triage Kubernetes CronJobs in a single tool call: whether runs happened and stayed on schedule, whether durations are trending slower or overrunning, which jobs have silently stopped succeeding, and why a given run failed, with evidence and stated basis for each verdict. It also lists active runs and recent failures while enforcing namespace allowlists and no write access.

Identifies resource-intensive processes on macOS across CPU, memory, and network usage, with tools for detailed process filtering and system overview.

See which AI agent and MCP server talks to what on Windows, with a rule-based security verdict

Provides AI agents with real-time ground truth about what's actually running on the machine, including live agent sessions, listening TCP ports, loaded daemons, and system stats. It reads directly from the OS via fixed commands, ensuring agents never rely on stale docs or transcripts.

Lets AI assistants query Dynatrace Managed environments — services, traces, metrics, pods, and problems — by running read-only GET requests through your already logged-in browser session, so no API tokens are needed. All traffic stays on your machine between the AI client, a local server, and a browser extension.

Provides transactional intelligence for AI agents, enabling safe tool execution with pre-flight invariant checks, sub-second filesystem snapshots/rollback, causal tracing, belief contradiction detection, and 15 native MCP tools for Claude Code.