Mason indexes your codebase into a persistent concept map linking features and flows to their implementing files, so AI agents can answer "where is X implemented" without running grep/glob. It also provides pre-edit impact analysis and generates CLAUDE.md files from structured analysis of git history, architectural file sampling, and test mappings.
A local-first, agent-agnostic MCP server that provides semantic search, persistent memory, and automated code review capabilities for development workflows. It leverages the Auggie SDK to offer advanced tools for codebase indexing, implementation planning, and deterministic static analysis.
A Model Context Protocol server that fetches up-to-date, version-specific documentation and code examples from libraries directly into LLM prompts, helping developers get accurate answers without outdated or hallucinated information.
Local-first MCP server that provides project context, verification gates, and structured tools for coding agents to discover knowledge, run diagnostics, and execute allowlisted commands within a repository.
A batteries-included MCP server providing common coding-agent tools like web search, page fetching, file system access, shell command execution, and Git integration, built in Python with a local-first, Unix-style philosophy.
A single-file MCP server that turns any folder into a workspace for AI agents, enabling file operations, code search, shell commands, background jobs, HTTP testing, headless browsing, PDF extraction, and screen capture.
A local MCP server that gives AI agents access to developer tooling — GitHub (read-only), documentation search, and web research — via stdio transport.
MCP server that equips AI agents with dev workflow tools including GitHub project management, conventional commits, visual regression testing, Jira/Confluence integration, and a persistent memory knowledge graph.