Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Critical Path Method scheduling and lock-aware parallel cohorts for MCP agents.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"cpm-planner": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add cpm-planner -- docker run -i --rm ghcr.io/praxec/cpm-planner:0.5.0In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"cpm-planner": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"cpm-planner": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto praxec bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.5.0) kam vor 11 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Docker auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Python MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Local-only Python MCP server for the Files.com API.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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
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