Murmur

Before you ship a pricing or launch change, find the objections that would kill it — ranked by blast radius, each tied to the simulated post behind it, inside the coding agent you already use, with no extra API keys.

What you get

• Surface the killing objections to a pricing, launch, policy, or crisis decision — each stated at its strongest, with the evidence and the decision it threatens.

• Argue the other side honestly: every dossier carries the counter-case, so an adversarial tool cannot talk you out of a good idea silently.

• Stay in your coding workflow with Claude Code, Codex, Gemini CLI, Cursor, Windsurf, and other MCP clients.

• Add no API keys or Murmur network calls; model use stays inside the host agent you already use.

• Trace every claim to the simulated post behind it, and every number to the run that produced it — the engine rejects drafts that invent figures.

• Replay the same seed, inputs, and host model to reproduce the same run.

See the shipped dashboard in 3 clicks

1. Get the repository. Clone it or download the repository archive.

2. Reveal the hidden folder. .murmur is a dot-folder, so macOS Finder hides it by default: choose Go → Go to Folder (Cmd+Shift+G), paste murmur-demo/.murmur/reports/pricing-reaction, and press Return. From the repository root, open murmur-demo/.murmur/reports/pricing-reaction/report-1.html also works on macOS; in Windows PowerShell use start .\murmur-demo\.murmur\reports\pricing-reaction\report-1.html; on Linux use xdg-open murmur-demo/.murmur/reports/pricing-reaction/report-1.html.

3. Open report-1.html. Double-click it to open the self-contained dashboard in your browser; no build or local server is required.

The 61 KB report covers Acme Cloud's 30% price increase with 7 supporters versus 5 opponents, five rounds of reaction, platform divergence, persona spotlights, risks with the simulated posts behind them, recommendations, limitations, and a post index. It makes no external requests and works offline.

Viewing on GitHub: GitHub displays this file as source rather than rendering it. Use Raw → Download raw file, then open the downloaded report-1.html locally.

Related MCP server: crowd-test-mcp

① Open the demo dashboard (10 seconds)

Double-click murmur-demo/.murmur/reports/pricing-reaction/report-1.html.

It is fully self-contained — no scripts, no external requests, works offline — and shows the simulated "Acme Cloud pricing change" projection: executive dashboard, multi-entity sentiment chart, faction donut + cards, platform divergence, persona spotlights, risk register with evidence, recommendations, and five appendices. The Markdown twin (report-1.md) is the same data in diffable form.

Then skim murmur-demo/DEMO-TRANSCRIPT.md to see the 34 tool calls that produced it.

② Connect it to your coding agent

One-time setup — pick either option:

# Option A — install from source (dist is pre-built inside the bundle)
cd murmur && npm install

# Option B — install from npm (the registry package — the primary route)
npm install -g murmur-mcp
murmur-mcp doctor                                # environment check
npx murmur-mcp doctor                            # …or run one-off, no global install

Then register the server with your client — e.g. Claude Code:

# after Option B (global binary on PATH)
claude mcp add murmur -- murmur-mcp serve
# …or via npx, no global install
claude mcp add murmur -- npx murmur-mcp serve

# after Option A (direct path — no global install needed)
claude mcp add murmur -- node /<path-to>/murmur-complete-v1.0.0/murmur/packages/server/dist/index.js serve

Also handy:

murmur-mcp templates                           # browse the 5 scenario packs
murmur-mcp init --client claude-code --write   # writes the client config for you

The server speaks plain MCP over stdio. murmur/README.md has the full 10-client install matrix (Codex, Gemini CLI, Antigravity, OpenCode, Hermes, Cursor, Windsurf, Cline/Continue, VS Code) with copy-paste snippets.

Once connected, ask your agent things like: "Simulate how r/devops reacts to our new pricing page" — the agent drives the 11 Murmur tools; the LLM you already pay for does the persona voices.

③ Develop, test, replay

cd murmur
npm install            # if you skipped Option A (dev toolchain: typescript, vitest, sqlite)
npm test               # 651 unit + golden tests (byte-identical replay, citation integrity)
npm run test:e2e       # 190 end-to-end: 188 market-twin contracts + integration + cleanroom, real SDK client ↔ server over stdio
npm run cleanroom      # zero-egress gate: no network APIs, no URLs, no key surfaces
npm run build          # rebuild dist from source (all 5 packages)

Replay the demo (rewrites murmur-demo/.murmur/ deterministically — same seed, same bytes, every time; needs the npm install above first):

node tools/demo-live.mjs

The scripts in tools/ resolve murmur/ and murmur-demo/ relative to their own location, so the bundle can sit anywhere on disk. scale-benchmark.mjs measures token budget and wall time at any scale: node tools/scale-benchmark.mjs 48 8.

Publishing to npm (when you're ready)

1. repository / homepage / bugs in murmur/packages/server/package.json already point at github.com/Ratz00007/murmur-mcp — the repo exists and is public; if you rename it, update those three fields plus the badge URLs at the top of this README.

2. Push the repo, add the NPM_TOKEN secret, and the release workflow (.github/workflows/release.yml) runs every gate and publishes with provenance — or simply npm publish from murmur/packages/server/.

The npm name murmur-mcp is published (murmur-mcp@1.0.0, Apache-2.0, npm registry).

The guarantees (why no API key)

• Host-Powered Inference — the server never calls an LLM and ships no network code. It prepares batched generation tasks; your coding agent's own LLM completes them as part of the session you already pay for.

• Deterministic — seeded RNG everywhere (mulberry32 + FNV streams, test-enforced). Same seed + same inputs = same world and the same report, byte for byte, enforced by golden replay tests.

• Cleanroom-verified — the gate asserts the published bundle contains no network APIs, no URLs, and no key/token surfaces. The HTML dashboard inherits this promise: open it offline and check for yourself.

Token cost

Murmur ships zero API keys — the server itself never calls an LLM. What a run consumes is host-LLM generations completed inside the coding session you already pay for:

• world + persona drafting tasks (ontology and persona-voice submissions),

• per-round action sets — at the defaults of 24 personas × 8 rounds that is on the order of 192 persona action-set generations per run,

• report drafting tasks (plan → draft → risks → recommendations → confidence),

i.e. order of hundreds of host-LLM generations per run at defaults.

Estimation method (the same one the e2e tests use): tokens ≈ chars / 4. Reproduce with node tools/scale-benchmark.mjs <personas> <rounds>.

Measured engine-side budget per full run (deterministic mock brain, so these are content-independent: they measure the task payloads the host must read and the shape of what it must write — not how verbose a real model is):

Input splits roughly: ontology ≈0.8k, personas ≈1.0k, per-round sim batches ≈7.9k–9.8k each, report task ≈5.0k. Output splits ≈ generations (9.3k at flagship) + report draft (≈1.6k). Report markdown ≈26k chars either way.

Measured from the bundled mock demo (murmur-demo/.murmur/ — mock brain, 12 personas · 5 rounds, a smaller run than the defaults above):

Real-LLM cost varies by model, provider pricing and prompt caching. The tables above are char-based estimates from deterministic runs — treat them as a ceiling on task payload size, not a bill. A run also costs your session's wall-clock time (the engine is 3s; the LLM round-trips are the slow part). Pending: median billed tokens/report from the first real-LLM gallery run — publish it in gallery/ and replace this line with the measured figure.

Where to read more

• METHODOLOGY.md — what a run is and is not, the metrics that restate each other, and how to argue with a report

• BENCHMARK.md — the pre-registered attempt to measure recall against real reactions, and what it found

• GOAL.md — the standing objective, success criteria, and the full build log with verification evidence

• gallery/ — published runs (real ones and clearly labeled mock measurements)

• murmur-demo/FLAGSHIP-DEMO.md — the real-LLM protocol and its status (blocked on model access, recorded honestly)

• murmur/GOOD-FIRST-ISSUES.md — ten scoped starter tasks for contributors

• docs/archive/ — superseded process docs (handoffs, worklogs, roadmaps, release checklists), kept for history