FocusMCP Marketplace

Official catalog of 68+ bricks for FocusMCP.

What's a brick?

A brick is an atomic MCP module with a well-defined scope. Examples: shell runs commands, echo replies messages, lastversion checks package versions.

Users compose bricks on demand — only the ones they need load into their AI's context.

Browse bricks

Use the FocusMCP CLI:

npm install -g @focus-mcp/cli
focus browse

Or view the raw catalog:

curl https://raw.githubusercontent.com/focus-mcp/marketplace/main/publish/catalog.json

Categories

• Files: fileread, filewrite, filelist, fileops, filesearch, filediff, smartread, multiread
• Code Intelligence: treesitter, symbol, callgraph, depgraph, cache, outline, refs
• Context: smartcontext, overview, compress, tokenbudget, contextpack
• Shell & Execution: shell, sandbox, batch
• Code Editing: rename, codeedit, inline
• Reasoning: thinking, planning, decision
• Search: textsearch, fts, semanticsearch
• Knowledge: knowledge, graphbuild, graphquery, graphcluster, graphexport
• Orchestration: dispatch, parallel, debate, review, research, agent, share, task
• Analytics: metrics, heatmap, savings
• Utilities: format, validate, convert, diagram, routes
• Workflows: onboarding, fullaudit, autopilot
• Version intelligence: lastversion

(This list is for humans — the full catalog is at catalog.json)

Measured economy

Static, reproducible benchmark on 79 tools across all 69 bricks (no LLM, no variance):

	Tokens
Native equivalent (raw Read, Grep, etc.)	366,912
FocusMCP bricks output	127,228
Saved	239,684 (−65.3%)

Top 5 most efficient tools (best replacement for native operations):

Tool	Native	Brick	Δ
treesitter.ts_reindex	8,522	4	−100.0%
treesitter.ts_index	5,645	7	−99.9%
symbol.sym_find	2,678	4	−99.9%
routes.rt_list	6,796	10	−99.9%
symbol.sym_bulk	8,237	12	−99.9%

Caveats (full transparency):

• 4 tools show marginal overhead (+2 to +28%) on tiny outputs where JSON envelope weighs more than the operation. Examples: fl_list, fr_head, fr_range, fmt_json.
• This is iso-call measurement: it captures payload economy, not multi-turn agent dynamics. Real agent sessions have variance and exploration costs that this benchmark doesn't model.
• See benchmarks/equivalence-report.md for the full per-tool table and methodology.

Reproduce: pnpm --filter @focusmcp/bench-harness exec tsx src/equivalence.ts

Break-even by tool category

Some bricks save tokens immediately, others need multiple calls to amortize their fixed cost (manifest loaded once per session = fixed overhead M+D).

Category	Tools	Break-even	Example
Always wins	outline, smartread, refs, compress, fts, textsearch, filesearch, overview, rename	N=1	outline.out_repo saves 23,226 tokens/call
Wins fast (≤10 calls)	—	≤ 10	—
Wins eventually (≤100 calls)	—	≤ 100	—
Never wins (single-shot)	fileread, filelist, multiread, filediff, format	∞	JSON envelope > operation savings

Session projection (50 varied calls, top-quartile mix): ~80% token savings vs native.

See full math model report.

Roadmap & improvements

Enhancement opportunities tracked in IMPROVEMENTS.md — architecture, performance, and new capabilities. Bugs and patches are in benchmarks/PATCH_QUEUE.md.

Contribute a brick

See CONTRIBUTING.md.

AI-assisted development

FocusMCP was built with heavy Claude Code assistance — its architecture, implementation, docs, and tests have all been co-authored with AI. We embrace this openly because:

1. Transparency matters — we'd rather disclose it than pretend otherwise
2. AI tooling is the context — we're building tools for AI agents, it makes sense to use them
3. Quality over origin — what matters is that the code is tested, reviewed, and working

Your AI-assisted contributions are welcome. We don't require you to hide the fact that Claude, Copilot, Cursor, or any other tool helped you. What we do expect:

• Tests pass, code is typed, lint is green
• You've read the diff and understand what the PR does
• Conventional Commits, clear PR description
• You can explain your design choices during review

See CONTRIBUTING.md for the full guidelines.

License

MIT