Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者による説明(英語の原文)
Deterministic Brotato theorycrafter MCP server: facts and math, not guesses.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
SPUDCOACH_DATA必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"spudcoach": {
"command": "uvx",
"args": [
"spudcoach"
],
"env": {
"SPUDCOACH_DATA": "your-value"
}
}
}
}ターミナルでこれを実行します
claude mcp add spudcoach --env SPUDCOACH_DATA=your-value -- uvx spudcoach動かす前に、空欄の値を入力してください。
プロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"spudcoach": {
"command": "uvx",
"args": [
"spudcoach"
],
"env": {
"SPUDCOACH_DATA": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"spudcoach": {
"type": "stdio",
"command": "uvx",
"args": [
"spudcoach"
],
"env": {
"SPUDCOACH_DATA": "your-value"
}
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
SPUDCOACH_DATA envこの設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント brendanlefebvre で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.14.1)がレジストリに届いたのは3 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにPython (uv)も必要です。
同じカテゴリには:Spec Driven Development, Remote Desktop Commander, NexusWindows
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
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.
Windows computer use in the background: desktop apps driven without your mouse or keyboard.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
A deterministic theorycrafter for Brotato, delivered as an MCP server you can chat with from Claude Code (and other MCP clients).
The design principle: a deterministic core holds the ground truth — weapon/item/character data, DPS formulas, stat mechanics — so the language model *looks facts up and computes* instead of recalling (and misremembering) them. Every tool returns a finished, verifiable answer or a structured error; there are no baked-in tier lists or opinions, only facts and math.
The dataset (data/brotato.json) is not committed — it is derived from copyrighted game files, so you build it yourself from a local Brotato install (see Building the dataset). A full build from Brotato 1.1.15.4 contains 202 weapons, 197 items, 50 characters, and 15 weapon-class sets.
uv for environment/dependency managementInstall dependencies:
uv sync
Build the dataset (needs a local extraction — see Building the dataset), then start the server:
uv run python build_dataset.py # writes data/brotato.json
uv run python -m brotato_coach.server # starts the MCP server over stdio
The server refuses to start without data/brotato.json and tells you to build it first.
Run the tests (the dataset-dependent integration test is skipped when no dataset is built):
uv run pytest # 89 tests (88 passed + 1 skipped without a built dataset)
uvx spudcoach --data /path/to/brotato.json
The dataset is never distributed — build your own from your Brotato install: uv run python build_dataset.py (see docs/extraction-setup.md). Game version auto-detects from the decompiled recovered/singletons/progress_data.gd, and generated_at defaults to the current UTC time — pass --game-version/--generated-at explicitly to override either. SPUDCOACH_DATA works as an env-var alternative to --data.
The MCP server is described by plugin/.mcp.json:
{
"mcpServers": { "spudcoach": { "command": "uv", "args": ["run", "python", "-m", "brotato_coach.server"], "cwd": "${CLAUDE_PLUGIN_ROOT}" } } }
The server reads the (locally built) data/brotato.json relative to its working directory, so it must run with the repository root as its `cwd` (the manifest handles this via ${CLAUDE_PLUGIN_ROOT} when bundled as a plugin), and you must build the dataset first.
To register it directly in Claude Code without packaging, add the server pointed at your checkout, e.g.:
claude mcp add spudcoach -- uv run --directory /path/to/spud-coach python -m brotato_coach.server
Once connected, just ask in natural language — the model routes your question to the tools below:
Claude Desktop can launch the server with uvx in two forms: fetch straight from this repo (auto-updates on restart, but needs git reachable — see the Windows note below), or point at a local checkout (nothing fetched at runtime; the most reliable form on Windows). Either way you supply your own locally built brotato.json — the dataset is never distributed.
uv on the machine running Claude Desktop (winget install astral-sh.uv on Windows, or the standalone installer).spud-coach server:%APPDATA%\Claude\claude_desktop_config.json~/Library/Application Support/Claude/claude_desktop_config.jsonFetch-from-repo form:
{
"mcpServers": { "spud-coach": { "command": "uvx", "args": [ "--from", "git+https://github.com/brendanlefebvre/spud-coach", "spudcoach", "--data", "C:\\Users\\ \\path\\to\\brotato.json" ] } } }
Point --data at your built dataset (SPUDCOACH_DATA works as an env-var alternative). On macOS use a POSIX path like /Users/ /brotato.json.
uvx not found)The git+https://… form makes uv shell out to a git executable. Claude Desktop does not pass your shell — or even your System `PATH` — to the MCP subprocess; it spawns servers with its own trimmed environment. (The PATH it prints in the logs is its command-resolution list, *not* what the child process receives.) So a git that runs fine in PowerShell, installed in a System-PATH directory, can still come back "not found" here — and a bare "command": "uvx" can fail to resolve for the same reason. Two fixes:
--from instead of --from git+…: git clone https://github.com/brendanlefebvre/spud-coach C:\Users\<you>\src\spud-coach
{
"mcpServers": { "spud-coach": { "command": "uvx", "args": ["--from", "C:\\Users\\ \\src\\spud-coach", "spudcoach", "--data", "C:\\Users\\ \\path\\to\\brotato.json"] } } }
Update later with git pull in that folder, then restart Desktop.
git+https form and add an env block that hands the child an explicit PATH — git, plus uv's bin and the winget-links dir: "env": {
"PATH": "C:\\Program Files\\Git\\cmd;C:\\Users\\ \\.local\\bin;C:\\Users\\ \\AppData\\Local\\Microsoft\\WinGet\\Links;C:\\Windows\\System32" }
If uvx itself still isn't found, also set "command" to its absolute path (Get-Command uvx to locate it, e.g. C:\Users\ \AppData\Local\Microsoft\WinGet\Links\uvx.exe).
All tools return a JSON object. Lookups that miss return {"error": "not_found", "did_you_mean": [...]}.
22 tools. Arguments marked ? are optional.
| Tool | Arguments | Returns |
|---|---|---|
read_me | — | Session-start primer: how Brotato's core loop works, the source-verified stat mechanics, and what every precomputed field assumes. Call once, before anything else |
| Tool | Arguments | Returns |
|---|---|---|
get_weapon | name, tier? | Weapon record incl. raw stat-aware fields, on-hit effects, and weapon-class sets; {matches:[...]} if tier omitted and several tiers match |
get_item | name | Item record: effects, tags, archetype, frozen_stat |
get_character | name | Character kit: wanted_tags, banned_item_groups, flat_bonuses, gain_modifiers, special_effects, class_bonuses, starting_weapon_pool |
get_weapon_class_set | class_name | Weapon-class set bonuses (Blade, Gun, Elemental, …), by equipped count |
list_weapons | scaling_stat?, tier? | {weapons:[...]} filtered summaries |
list_items | tag?, scaling_stat?, archetype?, tier? | {items:[...]} filtered summaries |
list_characters | scaling_stat?, wanted_tag?, banned_item_group?, special_effect?, class_bonus_set?, can_start_with?, detail? | {characters:[...]} — full records, not summaries (see below) |
get_filter_options | — | Valid filter values in the dataset: item tags, archetypes, scaling stats, tiers, weapon-class names, enemy abilities / attack kinds / zones, and the character filter vocabularies |
| Tool | Arguments | Returns |
|---|---|---|
weapon_dps | name, tier, stats, aoe_enemies_hit?, character?, weapon_count?, engagement_distance?, loadout?, apply_set_bonuses? | Realized, stat-aware DPS at your build, split into base_dps + proc_dps, with a cadence breakdown and the assumptions baked in |
compare_weapons | names_with_tiers, stats, plus the same optional arguments as weapon_dps | {ranking:[...]} sorted by DPS descending, at one shared stat block |
compare_merge_paths | weapon_name, path_a, path_b, stats? | Winner or crossover_rd for two tier-merge paths (lists of tiers) |
stat_gradient | weapons, stats, step?, character?, aoe_enemies_hit?, engagement_distance? | Which stat to buy next: stats ranked by the DPS gain from +step of each. DPS only — survivability is out of scope |
| Tool | Arguments | Returns |
|---|---|---|
evaluate_item_for_build | item_name, character_name, current_stats | Per-effect verdict — live / wasted / harmful — with reasons, plus a summary |
loadout_set_bonuses | weapon_names | Per-class set progress across a whole loadout: equipped count, active bonuses, and next threshold |
explain_stat | stat | Verified stat mechanics: caps, special behavior, neglectable / never-negative flags |
stat_display_value | character, stat, raw_value | Displayed value after the character's gain modifiers (e.g. Ranger RD 6 → 9) |
| Tool | Arguments | Returns |
|---|---|---|
get_enemy | name, wave? | Enemy record: base stats, per-wave stat slopes, attack profile, ability tags. Pass wave (1-20) to also resolve effective stats at that wave |
list_enemies | appears_in?, ability?, attack_kind? | {enemies:[...]} filtered summaries |
wave_composition | wave, danger? | Base-game (Crash Zone) composition for a wave: enemy groups, base counts, first-spawn timing, repeats |
| Tool | Arguments | Returns |
|---|---|---|
evaluate_run | path? or run_json? | One-call post-mortem of a whole Brotato run.json save: run context (character, wave, danger), realized stats, weapon-DPS ranking, set progress, and per-item verdicts |
check_dataset_version | — | game_version, generated_at, schema_version |
Batch lookups. get_weapon, get_item and get_character accept name as either a string or a list of strings. A list returns {"results": [...]} in the order given, each entry a record or its own not_found — so detail on several entities costs one round-trip, not N.
`list_characters` returns full records, unlike list_weapons / list_items, which return summaries. The whole 50-character corpus is only ~7.7K tokens, so a survey answers in a single call with no follow-up get_character; pass detail="summary" for {id, name} rows instead. Its scaling_stat filter is a derived union — characters have no scaling-stat field — matching a positive stat-gain modifier, a positive flat bonus, or a matching wanted_tag. Each matched record carries a scaling_match object naming which signal fired, e.g. Engineer {"gain_pct": 25, "flat": 10, "wanted": true} vs Technomage {"wanted": true}, so the derivation is visible rather than asserted. Characters *penalised* in that stat are excluded from the match.
starting_weapon_pool is the run-start weapon-selection pool, not a fixed starting weapon: most characters offer a dozen or more, Arms Dealer offers only the Pistol,
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