"Tool for saving context and understanding repository structure" matching MCP servers:

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.

Lets a human review what an AI agent is about to post to a chat tool (Slack first) before it is sent. Renders the draft, destination and warnings (@here/@channel, unresolved mentions, format issues) in a neutral local HTML preview; never sends anything.

Manage a fleet of OPNsense firewalls from an AI agent, inside guardrails it can't drive around. MCP server for central management of OPNsense firewall fleets. 129 tools across devices, config sync, tasks, schedules, templates, backups and remote consoles - destructive actions confirmation-gated, MCP-issued tokens lifetime-bounded, backup and storage secrets excluded from the toolset entirely.

Connect to your TTRPG campaign's repository and database. Instead of retrieving prose, its 48 tools (20 read, 28 write) return typed state: 14 entity schemas (NPCs, factions, locations, sessions, lore), relationship and knowledge-graph queries, wiki blocks, and a narrative-state bundle of open threads and canon facts.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

Enables autonomous coding agents to inspect Git commits, create pull requests, parse issue threads, and post reviews on GitHub repositories.

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Enables agent tools to validate queries, assess token costs, and extract structured datasets from unstructured text using multiple LLM providers.

MCP server that integrates with VictoriaMetrics vmanomaly API to enable AI-assisted anomaly detection, model management, and configuration generation.

MCP server that enables AI assistants to drive systematic literature review workflows, including screening, downloading, converting, and reviewing papers through natural language conversation.

Open source MCP server specializing in easy, fast, and secure tools for Databases.

A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.

Enables LLMs to interact with embedded devices by reading and writing Segger RTT data through a J-Link debugger.

An MCP orchestration layer that aggregates multiple MCP servers while exposing only 8 meta-tools, dramatically reducing context window usage, and provides SLOP scripting, event monitoring, and tool customization.

MCP server for UniFi Network Controller enabling AI assistants to manage UniFi infrastructure via natural language. It supports firewall rules, IPv6, and uses lazy/eager tool modes to minimize context usage.

Enables interaction with Bitbucket through the Model Context Protocol, allowing users to manage pull requests, add comments, review code, create tasks, and perform other repository operations using natural language.

An MCP server for codebase context that gives AI coding agents structural understanding through symbol graph, semantic search, blast radius, and convention detection tools.

Enables agents to reduce tool noise by exposing only two meta-tools for searching downstream MCP servers and calling their tools on demand. Connections are lazy and tool lists are cached, so configured servers can be used without loading every tool upfront.

Enables AI assistants to structurally understand a Moodle installation and its plugins, so they can answer codebase questions and scaffold plugins that follow existing conventions. It scans Moodle sources and generates Markdown context files, indexes, and MCP tools/resources/prompts for querying, diagnosing, and maintaining that understanding.