Best Brick AI Skills & MCP Servers

Focus your AI agents on what matters. 68+ bricks, 1 MCP server, modular context — from 200k to 2k tokens. Works with Claude Code, Cursor, Codex.

Serveur MCP pour piloter Bricks Builder (WordPress) depuis Claude/Codex — édition de pages, gestion d'éléments, audit technique, audit design visuel, upload optimisé WebP. Communauté Discord : https://discord.gg/rX22zHRzH

Knowledge graph builder — construct a graph of code entities (files, functions, types) and their relationships.

Structured planning — create plans with steps, dependencies, and time estimates.

The runtime behind FocusMCP — Registry + EventBus + Router composing MCP bricks on demand. Browser-compatible, zero-dep, TypeScript strict.

Structured multi-perspective debate — define positions, score arguments, find consensus, summarize.

Diagram generation — produce Mermaid, DOT/Graphviz, and ASCII diagrams from data.

Knowledge graph query — find nodes, traverse neighbors, find paths, filter by type.

Structured reasoning chains — think step by step, branch alternatives, revise conclusions, summarize.

Shared state for multi-agent — share context, files, results between agents, broadcast messages.

Conformance test runner for FocusMCP bricks — validates manifests, tool schemas, and runtime behavior.

Multi-language tree-sitter parser — supports ~50 languages including TypeScript, Python, PHP, Go, Rust, HTML, YAML, JSON, Markdown, Twig, SCSS, etc.

Structured code review — analyze code quality, security, architecture patterns, compare versions.

Autopilot workflow — autonomous task execution with reasoning, context management, and shell commands.

Batch execution patterns — run multiple operations sequentially, in parallel, or as a pipeline.

Full project audit workflow — code quality review, security scan, architecture analysis, and metrics report.

Agent registry — register AI agents with capabilities, list available agents, query capabilities.

Session metrics tracking — token usage, costs, duration, and per-tool statistics.

Persistent key-value memory — store and recall information across sessions as JSON files.

In-memory file cache — avoid redundant disk reads with mtime-based invalidation.

Knowledge base — index documents, search by content, fetch entries, purge stale data, rank by relevance.

Change impact analysis — given a file or symbol, find what files are affected by a change.

Project onboarding workflow — auto-discover project structure, conventions, and key files for a new contributor or AI agent.

Full-text search with TF-IDF ranking — index files, search with relevance scoring, suggest completions.