Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Critical Path Method scheduling and lock-aware parallel cohorts for MCP agents.
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.
Necesitas
¿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": {
"cpm-planner": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add cpm-planner -- docker run -i --rm ghcr.io/praxec/cpm-planner:0.5.0Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"cpm-planner": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}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": {
"cpm-planner": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}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.
Esta 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 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub praxec, 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 (0.5.0) llegó al registro hace 11 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Docker en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, Toggl 2.0
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.
Toggl 2.0 MCP server for tasks, projects, time entries, and workspace operations.
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.
cpm-planner is a Critical Path Method (CPM) planner for agent work, exposed as an MCP server. You submit a task graph and it computes the schedule: earliest and latest start and finish, slack, the critical path and the bottleneck tasks that gate completion. It then leases lock-aware cohorts of ready deliverables with disjoint file sets, so several workers can run in parallel without colliding. Plans can live in the repository as plan-as-code files with forkable, comparable variants; read-only tools lint, level against resource capacities and run Monte Carlo risk; earned-value tools track PV, EV, AC, SPI and CPI against a frozen baseline; and an optional AI review proposes verified schedule improvements. Any MCP client (Claude Code, Cursor, VS Code, Codex, a custom orchestrator, or a praxec workflow) drives it over stdio. It is a standalone tool with no dependency on praxec.
Prebuilt packages are published for Linux, macOS and Windows on x86_64 and ARM64.
The installers resolve your OS and CPU and download the matching archive. They verify its SHA-256 against the release's `checksums.sha256` before installing, then atomically replace the binary in a user-local directory. A checksum mismatch aborts with a non-zero exit and installs nothing. Re-running the installer upgrades in place.
# Linux / macOS -> ~/.local/bin/cpm-planner
curl -fsSL https://github.com/praxec/cpm-planner/releases/latest/download/install.sh | sh
irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1 | iex
The installer prints the absolute path it installed to. Use that path when you register the server with a GUI client.
Installer details: inspecting the script, PATH, upgrades and direct downloads
If you would rather read the script before running it, download, inspect, then run (see Pin a version for ):
# Linux / macOS
curl -fsSLO https://github.com/praxec/cpm-planner/releases/latest/download/install.sh less install.sh sh install.sh --version
# Windows
irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1 -OutFile install.ps1 Get-Content install.ps1 .\install.ps1 -Version
PATH. If the install directory is not on your PATH, the installer prints the exact line to add. Pass --add-to-path (-AddToPath on Windows) to have it do that for you, user-level only and never with sudo.
On Linux and macOS it appends the line only if it is not already there, to:
~/.zshrc for zsh;~/.bash_profile if it exists, else ~/.bashrc, for bash;~/.profile for other shells.For fish it prints fish_add_path for you to run instead of writing a file.
Downloads. The installers only accept https:// download URLs.
Upgrades. After upgrading, restart your MCP client so it launches the new binary. On Windows the old exe is renamed to cpm-planner.exe.old and removed on the next run.
Direct downloads. You can also download an archive yourself and check it against checksums.sha256; a machine-readable release-manifest.json is published too. All assets are at https://github.com/praxec/cpm-planner/releases/latest.
| Platform | Asset |
|---|---|
| Linux x86_64 | cpm-planner-x86_64-unknown-linux-gnu.tar.gz |
| Linux ARM64 | cpm-planner-aarch64-unknown-linux-gnu.tar.gz |
| macOS x86_64 | cpm-planner-x86_64-apple-darwin.tar.gz |
| macOS Apple Silicon | cpm-planner-aarch64-apple-darwin.tar.gz |
| Windows x86_64 | cpm-planner-x86_64-pc-windows-msvc.zip |
| Windows ARM64 | cpm-planner-aarch64-pc-windows-msvc.zip |
In the commands in this README, is a release tag such as v0.2.0. Pick one from the releases page.
# Linux / macOS
curl -fsSL https://github.com/praxec/cpm-planner/releases/latest/download/install.sh | sh -s -- --version
$env:PRAXEC_VERSION = ' '; irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1 | iex
& ([scriptblock]::Create((irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1))) -Version
With Node 18 or later, install the cpm-planner command from npm:
npm install -g @matthew-cochran/cpm
cpm-planner --version
The package is a small launcher. It downloads the release binary for your platform, verifies its checksum and caches it, during install or, where npm skips install scripts, on first run. The package version is the release version, so npm install -g @matthew-cochran/cpm@0.2.0 pins one. To run it without installing, use npx -y @matthew-cochran/cpm. For details and environment variables, see npm/README.md.
Build and install a tagged release with Cargo (Rust 1.99 or newer):
cargo install --git https://github.com/praxec/cpm-planner --tag <version> --locked
The crates.io listing lags behind the GitHub releases until the next cargo publish, so cargo install cpm-planner does not give you the current version yet. Install from the tag as above.
docker pull ghcr.io/praxec/cpm-planner
docker run -i --rm -v cpm-planner-data:/data ghcr.io/praxec/cpm-planner
-i keeps stdin open for the stdio transport.
The image sets CPM_PLANNER_DB=/data/cpm-planner.db and declares /data as a volume owned by the unprivileged app user, so a named volume persists plan state across runs. For a bind mount (-v "$PWD:/data"), run as yourself so the directory is writable: --user "$(id -u):$(id -g)". Pass -e CPM_PLANNER_DB=... only to override the path.
To register the image with a client, put docker in command and the rest in args. For example, in Claude Code:
claude mcp add cpm-planner --scope user -- docker run -i --rm -v cpm-planner-data:/data ghcr.io/praxec/cpm-planner
cpm-planner --version
It prints cpm-planner . To check the MCP side without a client, run the smoke script from the repository. It performs the MCP initialize and tools/list handshake and fails if plan.submit, plan.status or plan.get is missing:
node scripts/mcp-smoke.mjs /absolute/path/to/cpm-planner
Write a plan as .cpm-planner/plans/ / .json (a PlanGraph):
plan.lint {path: ".cpm-planner/plans/checkout/main.json"} — static checks, no state written.plan.sync {path: ".cpm-planner/plans/checkout/main.json"} — register the variant and return its plan_id.plan.status {plan_id} — schedule, critical path, ready set and locks.plan.sync tracks the file by content hash; plan.status reports definition_drift when the tracked file and the stored head graph disagree.
Freeze the baseline, record progress, then read the report (earned_pct is earned in proportion under earning_rule: "weighted"; fifty_fifty credits 50% once a deliverable is in progress or has any reported earned_pct, and the default zero_hundred earns at completion):
plan.baseline {plan_id} — freeze the CPM schedule and budgets as baseline 1.plan.mark_status {plan_id, deliverable_id, caller_id, status: {"status": "in_progress"}, earned_pct: 50, actual_effort_hours: 4} — report progress and actual cost.plan.ev {plan_id} — PV, EV, AC, SV, CV, SPI, CPI, EAC and alerts.plan.snapshot {plan_id, format: "markdown"} — append the reading and export a Markdown table.A ratio with a zero denominator is null and explained in undefined; alerts (SPI_BELOW_0_9, CPI_BELOW_0_9) compare the two latest stored readings.
Any MCP client can run cpm-planner over stdio: register the cpm-planner binary as the command, or, with Node 18 or later, npx -y @matthew-cochran/cpm. For a GUI client, use the absolute path the installer printed.
Install the agent skills (/cpm-plan and friends) with cpm-planner skills install --target --user (or --project ), where is claude, codex, cursor, copilot, gemini, all, or agents-md (project only).
docs/AGENT-INSTALL.md has the per-client steps (Claude Code, Claude Desktop, Codex, Cursor, VS Code, Gemini CLI and others), env blocks, verification and troubleshooting. llms.txt indexes the docs for LLMs.
| Tool | Does | |
|---|---|---|
plan.submit | Submit a task graph; returns a plan id (idempotent on the graph + caller). A prerequisite is a deliverable id string or an object `{id, consumes?, kind?: artifact\ | interface, lag_hours?}; both forms round-trip through plan.get. A deliverable may set milestone: true (an acceptance point, zero-length unless given an estimate or duration, reported in plan.status milestones). An optional earning_rule (zero_hundred, the default: 100% only when complete; fifty_fifty: 50% once in progress or any earned_pct is reported; weighted: the reported earned_pct) sets how earned value credits partial progress and is part of the plan's identity. A deliverable's optional duration_hours (calendar time) replaces effort as its scheduled length and a prerequisite's lag_hours delays the dependent's start; effort stays the cost basis. An optional estimate {optimistic, likely, pessimistic} (0 <= optimistic <= likely <= pessimistic) is a three-point effort estimate. Scheduled-length precedence: duration_hours > estimated_effort_hours > estimate.likely > 0 for a milestone > the estimator default; Monte Carlo samples the estimate only when neither duration_hours nor estimated_effort_hours is set. Limits: at most 5000 deliverables, and every hour value (effort, duration, lag, estimate) finite and between 0 and 1000000 (INVALID_GRAPH). With name (optional project/variant, variant defaults to main`) it registers a named variant instead of an unnamed plan. |
| plan.acquire_cohort | Atomically acquire up to N ready deliverables with mutually disjoint file sets (an owned_files entry may be {path, mode: "append"}; append claims on one path may be co-leased and appear in shared_paths; a file may be owned by several deliverables only if they are ordered by prerequisites, or all claims are append); optional ttl_seconds sets the lease TTL (clamped to the server maximum); optional ids and filter.metadata target deliverables; metadata.kind = "manual" deliverables are never leased; th
Solo se muestra el principio; lee el resto en el repo