"Understanding and Using Docker Logs" matching MCP servers:

Enables LLMs to explore and analyze UK Government BEIS inspect_ai evaluation logs directly from tools like Claude Code and Cursor. It provides capabilities to list logs, view evaluation summaries, and inspect conversation histories for specific samples.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

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Exposes Ghidra headless binary analysis capabilities via the Model Context Protocol, enabling AI assistants to decompile, analyze, and emulate binaries with 32 tools and multi-binary support.

Provides AI coding agents with five intelligence layers (dependency graph, git history, documentation, architectural decisions, code health) via nine MCP tools, enabling deep codebase understanding and reducing exploration cost.

Navigate, search, and edit large codebases, logs, and data files that exceed AI context limits.

Enables AI assistants to perform read-only introspection of Symfony applications, exposing routes, controllers, services, entities, database schema, migrations, logs, and more via MCP tools.

An MCP server that helps AI agents comprehend a codebase by providing tools for navigating, searching, and understanding code structure and history.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

An MCP server that gives Claude structural understanding of a codebase—dependency graph, entry points, complexity ranking, and import cycles—without reading every file into context.

Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.

Stop pasting your file tree into Claude. Give any AI assistant real architectural understanding of a codebase — local, private, zero‑config.

Enables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.

Integrates Google Gemini 3.1 with Claude Code to provide web search, large codebase analysis, multimodal image understanding, and creative brainstorming, leveraging Gemini's 1M token context and Google Search grounding.

Enables AI-assisted FlutterFlow development by reading, exploring, and understanding FlutterFlow projects through Claude and other MCP-compatible clients.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

Provides structural, queryable understanding of a Python codebase via MCP tools, enabling direct lookups for callers, dependencies, and class hierarchies without repeated grep/read cycles.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

An extensible MCP server that provides intelligent Python code analysis, navigation, and understanding capabilities for AI assistants like Claude.

Local-first MCP graph intelligence server providing RRF hybrid search, multi-hop traversal, source snippets, and rationale nodes for AI agents, without Docker or web UI.