Templetry MCP server
Commit 11 Nov 2026Memungkinkan AI agent untuk melakukan scaffolding, planning, update, dan memperluas proyek software menggunakan template yang dapat dikompilasi serta lazy pieces.
Juga dikenal sebagai Templetry/engine, engine
- Stars
- 0
- Tools
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- Unduhan, 30 hari
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- Versi terbaru
- 1.11.1
- Commit terakhir
- 11 Nov 2026
- Lisensi
- Apache-2.0
Apa itu Templetry MCP?
Templetry adalah MCP server. Memungkinkan AI agent untuk melakukan scaffolding, planning, update, dan memperluas proyek software menggunakan template yang dapat dikompilasi serta lazy pieces.
Ringkasan dibuat dari README proyek
Tentang Templetry MCP server
Templetry menyediakan engine khusus untuk mengelola lifecycle proyek software melalui template yang dapat dikompilasi. Ini memungkinkan developer dan AI agent untuk melakukan scaffolding repositori baru, merencanakan perubahan struktural, dan memperluas codebase yang sudah ada menggunakan sistem lazy pieces. Engine ini menggunakan proses three-way merge saat melakukan update, yang memastikan perubahan dari template terintegrasi tanpa menghapus editan manual yang telah Anda buat pada proyek tersebut.
MCP server ini beroperasi melalui stdio dan tidak memerlukan konfigurasi tambahan atau environment variables. Server ini menyediakan tujuh tools spesifik termasuk list_templates, get_form_schema, plan, render, update, list_pieces, dan add_piece. AI client seperti Claude Desktop, Cursor, atau VS Code dapat menggunakan tools ini untuk menelusuri katalog template, menghasilkan proyek yang siap pakai, atau mengadopsi unit fungsional baru ke dalam aplikasi yang sedang berjalan. Sistem ini dirancang secara deterministik, memastikan bahwa input yang sama akan selalu menghasilkan output yang identik secara byte.
Apa yang bisa Anda lakukan dengannya
- 1Scaffold proyek baruAI agent dapat menelusuri katalog template dan menghasilkan repositori lengkap yang siap pakai berdasarkan kebutuhan spesifik.
- 2Memperluas codebase yang sudah adaDeveloper dapat menginstruksikan agent untuk mengadopsi modul fungsional baru, yang disebut sebagai pieces, ke dalam proyek yang sedang dalam tahap pengembangan.
- 3Sinkronisasi update templateAgent dapat melakukan three-way merge untuk memasukkan fitur template baru tanpa menimpa kode kustom milik developer.
- 4Verifikasi integritas proyekSistem dapat menjalankan perintah verifikasi di dalam container Docker untuk memastikan template yang di-render dapat dikompilasi dengan benar.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Konfigurasi
TEMPLETRY_DEMO_CAST
| Nama | Deskripsi | Tipe |
|---|---|---|
| TEMPLETRY_DEMO_CAST | — |
Pengecekan otomatis
Berjalan 14 Nov 2026Laporan lengkap →Belum diverifikasi. Sebuah server memerlukan skor 80, tidak ada temuan serius, dan data yang cukup untuk kami periksa. Cara kerja checks
- Source repository terhubung dan dapat dijangkautempletry/engine is public and reachablegithub.com/templetry/engine ↗Lolos
- Paket menyebut repo ini sebagai sumbernyaTidak diperiksa
- Provenance build (atestasi Sigstore)Tidak diperiksa
- Lisensi open source (OSI)Apache-2.0spdx.org/licenses/Apache-2.0.html ↗Lolos
- Ada commit atau release dalam satu tahun terakhirLast commit or release 2026-11-11github.com/templetry/engine ↗Lolos
- OpenSSF ScorecardTidak diperiksa
- Nama package tidak mirip dengan package populerTidak diperiksa
- Tidak ada advisory kritis di paket maupun dependensinyaTidak diperiksa
- Tidak ada instruksi tersembunyi untuk model0 tool descriptions and the README read; no override phrases, credential paths or hidden charactersLolos
- Tools yang bersifat destruktif telah diberi labelTidak diperiksa
- Endpoint remote menjawab handshake MCPTidak diperiksa
README
Lihat di GitHubTempletry Engine
The core of Templetry: a pure Go library + thin CLI that renders ready-to-work repositories from compilable templates.
Scaffold, edit, then update: the template's new commit is merged three ways (base render, your project, new render), so its changes land and your edit stays. Recorded from the real CLI against a small fixture template (see Development).
Status: 🚀 stable v1 (latest release) — the manifest, directives, answers-file and registry schemas plus the CLI surface are stable public API under the compatibility policy (ADR-0013).
Since v1.0: templetry update (three-way merge), templetry verify in Docker, lazy pieces (pieces/add), YAML/TOML patches, feature requires/conflicts and presets, multi-forge template hosting (github:, gitlab:, gitea: source schemes) and the templetry-mcp server. Since v1.10 forms carry a taxonomy (kinds/languages/frameworks) and templetry list filters on it. Output paths are hardened against escapes and platform quirks; the directive scanner is fuzz-tested.
Principles (from the wiki)
- The engine doesn't know what a framework is — all knowledge lives in each template's
template.yml(ADR-0002). - Templates compile — three transformation mechanisms: identity-map renaming, comment directives, structured patches (ADR-0003).
- Pure core, effectful edges — deterministic: same inputs, byte-identical output.
Architecture
| Package | Responsibility |
|---|---|
manifest |
Parse + validate template.yml, derive casings |
source |
Obtain the template as an in-memory FileSet |
planner |
(manifest, inputs, FileSet) → Plan — pure |
ops |
Operation types as data |
render |
Execute a Plan against a virtual file tree |
verify |
Run the template's verify command in Docker |
update |
The update cycle: re-render with recorded inputs, diff, three-way merge |
answers |
The .templetry-answers.yml record: one deterministic reader/emitter |
piece |
Lazy pieces (ADR-0014): decoupled units with their own lifecycle |
cmd/templetry |
Thin CLI: list, init, plan, render, verify, update, pieces, add, version |
cmd/templetry-mcp |
MCP server (stdio): the engine's verbs as tools for AI agents |
Design rationale: study I · study II.
Install
Pick one (Homebrew, Scoop, the install script and the release archives also ship templetry-mcp):
- Homebrew (macOS, Linux) —
brew install Templetry/tap/templetry(tap). - Scoop (Windows) —
scoop bucket add templetry https://github.com/Templetry/scoop-bucketthenscoop install templetry(bucket). - Install script (macOS, Linux) —
curl -fsSL https://raw.githubusercontent.com/Templetry/engine/main/install.sh | sh(seeinstall.sh;TEMPLETRY_VERSIONpins a tag,TEMPLETRY_BIN_DIRpicks the folder). - Binary — download your platform's executable from Releases, rename it to
templetry(ortempletry.exe) and put it on yourPATH. - Go toolchain —
go install github.com/Templetry/engine/cmd/templetry@latest(installs to$(go env GOPATH)/bin). - From source — clone and
go build -o templetry ./cmd/templetry.
Then:
templetry list # browse the official catalog
templetry list --kind database --language go --tags # filter it
templetry init kmp/single-module --out my-app \
--set "project_name=My App" --set "base_package=com.me.myapp"
templetry plan --template <dir> --set key=value --feature name
templetry render --template <dir> --out <dir> --set key=value
templetry verify --template <dir> --set key=value # render to a temp dir and compile it in Docker
templetry update ./my-app # preview a template update (--apply to write)
templetry pieces ./my-app # lazy pieces the template ships
templetry add axios-api ./my-app --set api_base=/v2 # adopt a piece into a living project
MCP server (AI agents)
templetry-mcp (in the same releases) speaks the Model Context Protocol over stdio — no dependencies, no configuration. It exposes seven tools: list_templates, get_form_schema, plan, render, update, list_pieces and add_piece, so an agent can browse the catalog, scaffold a ready-to-work project, adopt pieces and keep it all updated. Copy-paste configuration for the common clients follows; the server is a plain executable, so any other MCP client works the same way. It is listed on the official MCP registry as io.github.Templetry/templetry-mcp and published automatically after each release.
Where the binary is
If templetry-mcp is on your PATH, use the bare name templetry-mcp as the command. Some apps (Claude Desktop especially) do not inherit your shell PATH, so give them the absolute path:
| Installed with | Path |
|---|---|
| Homebrew, Apple Silicon | /opt/homebrew/bin/templetry-mcp |
| Homebrew, Intel Mac / Linux | /usr/local/bin/templetry-mcp (Linux brew: /home/linuxbrew/.linuxbrew/bin/templetry-mcp) |
install.sh |
~/.local/bin/templetry-mcp (or /usr/local/bin/templetry-mcp when run as root); write the full home path in JSON, ~ is not expanded |
| Scoop (Windows) | C:\Users\<you>\scoop\shims\templetry-mcp.exe |
| Downloaded release binary | wherever you put it |
where templetry-mcp (Windows) or command -v templetry-mcp prints the right one.
Claude Code
claude mcp add templetry -- templetry-mcp # this project, private to you
claude mcp add --scope user templetry -- templetry-mcp # every project
claude mcp list
Claude Desktop
Edit claude_desktop_config.json (Settings > Developer > Edit Config; macOS ~/Library/Application Support/Claude/, Windows %APPDATA%\Claude\) and restart the app:
{
"mcpServers": {
"templetry": {
"command": "/opt/homebrew/bin/templetry-mcp"
}
}
}
On Windows use "command": "C:\Users\<you>\scoop\shims\templetry-mcp.exe".
Cursor
.cursor/mcp.json in a project, or ~/.cursor/mcp.json for all projects:
{
"mcpServers": {
"templetry": {
"command": "templetry-mcp"
}
}
}
VS Code
.vscode/mcp.json in the workspace (or "MCP: Open User Configuration" for every workspace). Note the key is servers, not mcpServers:
{
"servers": {
"templetry": {
"type": "stdio",
"command": "templetry-mcp"
}
}
}
The server takes no arguments and no environment; to work against a private catalog the agent passes registry on each tool call.
Documentation
Usage guides live in the wiki: getting started, using templates, using pieces, keeping projects updated, authoring templates, AI agents and MCP. Normative behaviour is in spec/.
Development
go build ./...
go test ./... # CI also runs -race and golangci-lint v2
update merges with git merge-file, so git must be on PATH for its tests. No test touches the network or needs Docker:
internal/forgetestis an in-memory GitHub (codeload tarballs and the commits API) behindhttp.DefaultClient, soinit→ edit →updateruns end to end against templates the test publishes, commit by commit.internal/fakedockerputs a copy of the test binary onPATHasdocker, recording whatverifywould have run.- Both binaries are tested in-process:
templetrythroughrun(args, stdout, stderr)andtempletry-mcpthroughserve(in, out), a JSON-RPC session over pipes.
The demo above is recorded the same way, so it always shows real output. To regenerate it:
TEMPLETRY_DEMO_CAST=$PWD/docs/demo.cast go test ./cmd/templetry -run TestRecordDemo -count=1
npx svg-term-cli@2.1.1 --in docs/demo.cast --out docs/demo.svg --window --no-cursor
License
Apache License 2.0 — use it for anything, including commercially, with an explicit patent grant. The templates and the desktop app live in their own repos under MIT.
Pertanyaan tentang Templetry MCP
Apa yang bisa dilakukan AI agent dengan Templetry MCP server?
Agent dapat menelusuri katalog template, melakukan scaffolding proyek yang siap pakai, mengadopsi pieces tertentu, dan menjaga proyek tetap terupdate.
Bagaimana proses update bekerja di Templetry?
Siklus update menggunakan three-way merge (base render, proyek Anda, dan render baru) untuk memastikan perubahan template tergabung dengan baik sambil tetap mempertahankan editan lokal Anda.
Apakah MCP server ini memerlukan konfigurasi atau environment variables?
Tidak, server ini berkomunikasi menggunakan Model Context Protocol melalui stdio tanpa memerlukan dependensi atau konfigurasi tambahan.