Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Scaffold, plan, update and extend projects from Templetry templates.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Download the maker’s .mcpb file
The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.
Run this in your terminal
Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified with the GitHub account Templetry, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (1.11.1) reached the registry 7 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, yontu
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Türkçe yazı denetimi: TDK kuralları, yapay zekâ izleri, metin türüne göre üslup.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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](https://github.com/Templetry/engine/releases/latest)) — 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.
template.yml (ADR-0002).| 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.
Pick one (Homebrew, Scoop, the install script and the release archives also ship templetry-mcp):
brew install Templetry/tap/templetry (tap).scoop bucket add templetry https://github.com/Templetry/scoop-bucket then scoop install templetry (bucket).curl -fsSL https://raw.githubusercontent.com/Templetry/engine/main/install.sh | sh (see install.sh; TEMPLETRY_VERSION pins a tag, TEMPLETRY_BIN_DIR picks the folder).templetry (or templetry.exe) and put it on your PATH.go install github.com/Templetry/engine/cmd/templetry@latest (installs to $(go env GOPATH)/bin).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 --set key=value --feature name templetry render --template --out --set key=value templetry verify --template --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
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.
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\ \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 mcp add templetry -- templetry-mcp # this project, private to you
claude mcp add --scope user templetry -- templetry-mcp # every project claude mcp list
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\ \scoop\shims\templetry-mcp.exe".
.cursor/mcp.json in a project, or ~/.cursor/mcp.json for all projects:
{
"mcpServers": { "templetry": { "command": "templetry-mcp" } } }
.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.
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/.
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/forgetest is an in-memory GitHub (codeload tarballs and the commits API) behind http.DefaultClient, so init → edit → update runs end to end against templates the test publishes, commit by commit.internal/fakedocker puts a copy of the test binary on PATH as docker, recording what verify would have run.templetry through run(args, stdout, stderr) and templetry-mcp through serve(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
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.