"How to work with local files" matching MCP servers:

A local, read-only stdio MCP server that lets a Grok Build CLI agent check how much of its weekly SuperGrok / Grok Build usage pool is left, when the period resets, and its Extra Usage cap via tools like get_budget. It reads the existing grok login session and returns per-product figures as JSON, mirroring the TUI /usage screen, so agents can pause before expensive work when quota runs low.

Matomo MCP Server to Work fast with Analytics

Competitive intelligence platform with 24 tools. Monitor competitor pricing, content, positioning, tech stacks, and AI visibility — track how ChatGPT, Claude, and Gemini rank your brand.

A fail-closed MCP server for Msty Studio on macOS that provides read-only diagnostics and an optional bounded local-generation tool, without accessing chats, keys, or configuration.

MCP server for querying and analyzing TensorBoard event files, enabling AI agents to extract scalar data, statistics, and correlations without a running TensorBoard server.

Enables AI coding assistants to search local log files, inspect Docker containers, and clean up JSON payloads through a local stdio MCP server.

MCP server that stream-parses NDJSON log files without loading them into memory — filter by pattern, detect error spikes via Z-score analysis, summarize severity timelines by time window.

Enables monitoring and analysis of local application log files with real-time tailing, error tracking, and search capabilities. Perfect for debugging Node.js applications, web servers, or any application that writes to log files through natural language commands.

MCP server enabling local network interaction, shell command execution, system resource monitoring, and SSH-based remote device management.

Enables MCP-aware AI agents to query the local machine's health over stdio, retrieving CPU, memory, disk, and load-average metrics along with boot time and the top processes by CPU usage. It gives coding agents a structured, protocol-standard way to answer questions like whether it is safe to run a build or what is currently consuming resources.

A Model Context Protocol server that enables AI tools to automatically log their activities with detailed metrics like timestamps, token usage, and costs into daily markdown worklog files.

A Model Context Protocol server that provides AI assistants access to AWS CloudWatch Logs, enabling browsing, searching, summarizing, and correlating logs across multiple AWS services.

Provides structured, tool-based access to a Honeybadger account's API, enabling users to browse projects, inspect faults and notices, work with dashboards, alarms, and check-ins, and retrieve reference documentation such as BadgerQL. Runs in read-only mode by default, with optional write and delete operations, and can connect either through the hosted OAuth endpoint or a self-hosted stdio/Docker deployment.

Enables an AI client to work with the desktop-selected process’s typed memory searches, refinements, live watches, snapshot comparisons, progress, and cancellation through a local stdio MCP connection.

Enables real-time data streaming through Server-Sent Events with timestamp broadcasting and server monitoring capabilities. Optimized for deployment on Render.com with health checks and status endpoints.

Enables users to manage sites, review traffic, and work with sitemaps through Microsoft's webmaster API.

A local MCP server for managing SaaS projects with planning, operations, and secrets, designed to work with Claude Code via stdio. It provides tools for kanban, health monitoring, and secure secrets management, all logged for AI transparency.

Enables an AI assistant to diagnose on-premise Windows pm2-managed Node/Nitro services by answering read-only questions about deployed builds, process liveness, logs, current configuration, and whether the running process started with the configuration currently on disk.

A local MCP server that lets any MCP-compatible AI client manage a standalone World of Warcraft private server, including server control, database operations, NPC/quest/loot management, and more.

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

MCP server that reads local OpenClaw session files to provide token usage and cost data without any network calls or authentication.

Connects AI assistants to a hosted website-checking service so they can retrieve detected issues such as typos, broken links, slow pages, and expiring domains or certificates, then edit site code, mark items fixed or ignored, and request rechecks after publishing.

Logs LLM interactions to local CSV or Markdown files with automatic categorization for detailed tracking. It provides an interactive HTML dashboard to visualize query volume, model usage, and category distribution.

A 100% local development monitoring tool that captures browser console logs, network requests, and backend server output for analysis by AI assistants via MCP. It enables LLMs to debug applications by providing structured, real-time access to full-stack log data and persistent local storage.

A read-only MCP server that gives Claude safe access to Kubernetes clusters, enabling listing, describing, and monitoring resources without mutation risks and with secret masking.

Enables agents to open and update session work, report goals, tasks, blockers, outcomes, and subagent contributions, and resume compact context after interruptions. A local dashboard then visualizes multiple sessions and live updates without requiring additional model calls.

This MCP server exposes industrial maintenance and work-order intelligence tools, allowing users to search assets, retrieve and correlate alarm events, and query CMMS work orders through a standardized protocol.

Enables read-only querying of a local Grafana Loki instance, providing log search, service listing, and health check capabilities.

Enables local research workflows (paper discovery, relevance scoring, digests) and homelab monitoring (Prometheus, logs) through an MCP server, using local LLM inference via Ollama with no cloud dependencies.

Enables uploading PyPTO swimlane files and querying task durations and dependencies for performance analysis with swimlane diagrams in Codex.