Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Cost-parity bench for agent loops: $/success and time across model mixes, with verifiable receipts.
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.
ANTHROPIC_API_KEY, OPENAI_API_KEYValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"plzebo": {
"command": "npx",
"args": [
"-y",
"plzebo"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}Run this in your terminal
claude mcp add plzebo --env ANTHROPIC_API_KEY=your-value --env OPENAI_API_KEY=your-value -- npx -y plzeboFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"plzebo": {
"command": "npx",
"args": [
"-y",
"plzebo"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}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.
Paste into .vscode/mcp.json in your project
{
"servers": {
"plzebo": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"plzebo"
],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value"
}
}
}
}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.
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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 7, 2026
The registry name was verified with the GitHub account wkoverfield, 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.
You need to fill in: ANTHROPIC_API_KEY, OPENAI_API_KEY
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 (0.2.0) reached the registry 3 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, ScienceShot science reference
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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.
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Cost-parity bench for agent loops. Point it at two or more ways of getting the same task done (a cheaper model mix, a different setup, a tool on vs off) and it measures which one delivers the outcome for less, in dollars and in minutes, and whether the gap is real or noise. Runs locally, on your own keys or your own subscription. An agent can call it on itself over MCP.
The headline metric is cost per successful outcome: total metered spend divided by successes, compared across arms with a 95% confidence interval. Time per successful outcome is reported beside it. When the data cannot support a claim, plzebo says so instead of making it.
claude-* call to each arm's model.sk-placebo-...) and a redirected base URL; the gateway swaps in the real key upstream and records provider-reported token usage per call. A call that bypasses the gateway fails at the provider instead of billing invisibly, and a trial with zero metered calls is flagged, never compared.plzebo verify receipt.json recomputes every derived number from the raw rows and reports any divergence.Node 22.6 or later (the CLI runs its TypeScript sources through Node type stripping; the launcher supplies the flag on versions that need it). Git, plus whatever your arms and oracle need (for the examples here: python3).
npx plzebo run spec.yaml --estimate # prices the run from prior receipts; spends nothing
npx plzebo run spec.yaml # runs the experiment npx plzebo verify # recompute a receipt's claims from its raw rows
A spec:
name: sonnet-vs-opus
fixture: repo: ./my-fixture-repo # a git repo; every trial gets a fresh clone ref: main task: "Make the failing tests in this repo pass. Do not edit the tests." arms: sonnet: cmd: claude --print "$PLZEBO_TASK" --dangerously-skip-permissions --strict-mcp-config --mcp-config '{"mcpServers":{}}' --model claude-sonnet-5 auth: passthrough # runs on your logged-in Claude subscription rewrite: claude-*: claude-sonnet-5 # pin every call this arm makes, including auxiliary ones opus: cmd: claude --print "$PLZEBO_TASK" --dangerously-skip-permissions --strict-mcp-config --mcp-config '{"mcpServers":{}}' --model claude-opus-4-7 auth: passthrough rewrite: claude-*: claude-opus-4-7 baseline: opus oracle: cmd: [python3, -m, unittest, discover, -s, tests, -t, .] k: 3 trialTimeoutMs: 600000 budgetUsd: 30 # hard kill switch enforced at the gateway
Every run prints its cost estimate before the first trial. A run without a budget is refused. Start with a small K: the report on an inconclusive run prints the exact K that would resolve it and the estimated cost, and plzebo extend --to-k K reuses the trials you already paid for. A receipt can be extended once, and the extension is disclosed inside it.
auth: placebo (default): you export ANTHROPIC_API_KEY or OPENAI_API_KEY; arms only ever see placebo keys. Strongest metering guarantee: a bypassing call fails loudly.auth: passthrough: the arm uses its own login. No API credits are spent; USD is imputed from metered tokens at list rates and the receipt discloses this. Tokens are the ground truth either way. Verified for Claude Code, which honors ANTHROPIC_BASE_URL under subscription auth. Codex under ChatGPT auth routes to its own backend and ignores OPENAI_BASE_URL, so subscription-authed Codex is unmeterable; run Codex arms in API-key mode via the codex-cli template instead.Two caveats for passthrough arms. A trial that bypasses the meter is excluded rather than falsely counted, but the tokens it consumed upstream are real; the receipt cannot see them. And a personal login carries personal agent configuration: instructions, hooks, and memory files travel with it, which can inflate costs and, for agents that act on standing instructions, cause writes outside the trial workspace. Prefer isolated-config templates when comparing loops rather than your own setup.
Every cost node in a receipt records two models: role, the model the loop REQUESTED, and model, what the arm's rewrite rules resolved it to. A loop that requests different models for different jobs (a main session on one model, subagents on another) therefore encodes its role structure into the receipt, and arms that share the same command can bind those roles differently through rewrites alone:
arms:
mixed: cmd: sh scripts/arms/subagent-mix.sh # opus main session + a worker agent definition requesting sonnet auth: passthrough rewrite: claude-opus-*: claude-opus-4-7 # orchestrator stays opus claude-sonnet-*: claude-sonnet-5 # workers stay sonnet claude-*: claude-opus-4-7 # catch-all LAST: wildcard rules match in order after exact keys all_opus: cmd: sh scripts/arms/subagent-mix.sh # byte-identical loop; only the mapping differs auth: passthrough rewrite: claude-*: claude-opus-4-7
The packaged scripts/arms/subagent-mix.sh shows the Claude Code shape: a headless session with an inline --agents definition whose worker requests a different model than the main session. Adapt the agent prompt and models to your task.
A model-mix comparison is only valid when the loop actually delegated: a trial whose cost tree has no worker-role calls measured a solo run, not a mix.
npx plzebo fidelity <receipt.json|run-dir> # per-trial requested -> resolved counts
npx plzebo fidelity --worker "claude-haiku-*"
fidelity exits 1 if any completed trial never made a worker-role call, and prints each trial's call counts, tokens, and imputed spend grouped by requested and resolved model, so the mix an arm claims is checkable from its receipt.
The gateway speaks two wire formats: Anthropic and OpenAI-compatible, which covers most open-weight serving (Ollama, vLLM, OpenRouter, Together, and similar). Arms can override providers individually:
arms:
local-qwen: cmd: my-loop --task "$PLZEBO_TASK" upstreams: openai: http://127.0.0.1:11434 # any OpenAI-compatible server gpt: cmd: my-loop --task "$PLZEBO_TASK" keysFrom: openai: OPENAI_API_KEY # env var NAME; specs never contain key values
Constraint: plzebo can only meter loops that authenticate with a key or token and honor a redirectable base URL (ANTHROPIC_BASE_URL / OPENAI_BASE_URL).
npx plzebo-mcp serves four tools on stdio: measure_start, measure_extend, measure_status, and measure_result. Starting a measurement is two-phase: the first call validates the request and returns a cost estimate plus a one-time confirm token while spending nothing; only a second call presenting that token runs trials. A budget is mandatory, and MCP arms come only from named templates (claude-cli, claude-subscription, codex-cli), never raw shell. Job state and receipts persist under .plzebo/, so verdicts survive the session that produced them.
A receipt is a single-task result under a stated model mapping. It is not a model capability ranking and does not generalize beyond its fixture; the estimand text inside the receipt says exactly this. USD figures on passthrough arms are imputed at pricing-table rates from metered tokens, not billed spend, and the pricing table version travels in the receipt so the numbers stay recomputable after prices change.
MIT