Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Deterministic Brotato theorycrafter MCP server: facts and math, not guesses.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
SPUDCOACH_DATANecesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"spudcoach": {
"command": "uvx",
"args": [
"spudcoach"
],
"env": {
"SPUDCOACH_DATA": "your-value"
}
}
}
}Ejecuta esto en tu terminal
claude mcp add spudcoach --env SPUDCOACH_DATA=your-value -- uvx spudcoachCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"spudcoach": {
"command": "uvx",
"args": [
"spudcoach"
],
"env": {
"SPUDCOACH_DATA": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"spudcoach": {
"type": "stdio",
"command": "uvx",
"args": [
"spudcoach"
],
"env": {
"SPUDCOACH_DATA": "your-value"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
SPUDCOACH_DATA envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub BrendanL79, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (0.11.0) llegó al registro hace 4 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Tool Guardian
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
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.
Too many MCP servers eating your context? A router that loads each tool's schema only when needed.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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,
Solo se muestra el principio; lee el resto en el repo