"Creating an app from files" matching MCP servers:

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.

MCP server for Drumbeats monitoring. Enables creating monitors, triaging incidents, and running HTTP/SSL/DNS checks using natural language from any AI client.

An evidence-first Prometheus MCP server that lets AI agents query production telemetry with detailed receipts including exact PromQL, timing, series count, and explicit warnings for empty, high-cardinality, or unreliable data.

Enables AI agents to manage prompts and sessions via the ai-agent-logs API, with tools for health checks, login, listing/creating prompts, and retrieving sessions.

A read-only Kubernetes MCP server that enables developers to inspect app status, view pod events, and fetch container logs from dev clusters without requiring full kubectl access.

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.

MCP server for Uptrack uptime monitoring. Manage monitors and incidents from AI agents like Claude, ChatGPT, and Cursor.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

Records runtime events from any app via zero-code instrumentation and exposes them to LLMs through MCP for autonomous debugging.

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 LLMs to analyze logs by extracting patterns, redacting secrets, and providing token-efficient summaries from files, Docker containers, or journald.

Exposes configured log files as MCP tools, enabling agents to list, query, and follow logs from local and SSH sources.

Transforms Elasticsearch into an AI-powered observability engine for analyzing logs, APM traces, and system metrics through natural language.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

Enables AI assistants to inspect React Native app performance including memory, rendering, frames, and network requests, providing plain-language summaries and shareable reports.

An AI-powered server that analyzes system log files to identify errors/warnings and recommend fixes using FastMCP, LangGraph ReAct agents, and Anthropic Claude.

A read-only MCP server that exposes Azure incident-investigation tools for a single App Service and its Application Insights telemetry, enabling an agent to correlate deployment history, platform metrics, and transaction traces.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

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.

Compresses log files into templates and statistics using Drain3, and exposes them to AI assistants via an MCP server for efficient log monitoring and anomaly detection.