crowdos-mcp 0.7.0

Model Context Protocol server for CrowdOS — synthetic focus groups as agent-callable tools.

CrowdOS MCP Server

Synthetic focus groups as agent-callable tools. Exposes the CrowdOS developer API as Model Context Protocol tools so AI agents (Claude Desktop, Cursor, Cline, LangGraph, CrewAI, AutoGPT, Devin, etc.) can run synthetic public-opinion research with a single tool call.

What this gives you

Your agent can now do things like:

> Run a focus group on whether companies should mandate 4-day weeks.
   Use 200 agents from the us_general_population preset.

[tool: run_focus_group]
{
  "id": "ad4b3736-...",
  "sentiment_summary": {
    "positive_pct": 71.5, "negative_pct": 18.0, "neutral_pct": 10.5,
    "positive": 143, "negative": 36, "neutral": 21
  },
  "sample_responses": [
    {
      "agent_name": "Maria Chen", "age": 34, "occupation": "Software engineer",
      "sentiment": "positive",
      "reasoning": "It would be great for parents — three full days with the kids ..."
    },
    ...
  ]
}

Installation

pip install crowdos-mcp

Then mint a free sandbox API key at https://crowdos.ai/developers — no credit card required for the free tier (50 agents per study, 5 req/min).

Configure for Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "crowdos": {
      "command": "crowdos-mcp",
      "env": {
        "CROWDOS_API_KEY": "crowd_..."
      }
    }
  }
}

Restart Claude Desktop. The CrowdOS tools should appear in the slash-command picker.

Configure for Cursor

Settings → MCP Servers → Add. Same env block as above; command = crowdos-mcp.

Configure for Cline (VS Code)

Settings → Cline → MCP Servers → Edit JSON:

{
  "mcpServers": {
    "crowdos": {
      "command": "crowdos-mcp",
      "env": { "CROWDOS_API_KEY": "crowd_..." }
    }
  }
}

Tools exposed

Tool	What it does	Auth
run_focus_group	Synthetic poll on a topic, returns sentiment + quotes	required
run_debate	Multi-round synthetic debate, returns convergence + key arguments	required
compare_options	Synthetic A/B/C/D test on 2-4 text options, returns leaderboard	required
preview_cost	Estimate cents + seconds before running (free to call)	required
list_demographic_presets	Discover available audience templates	required
get_simulation	Fetch full results of a previously-run study	required
crowd_sample	Browse the public CrowdOS crowd (sanitized)	none

run_focus_group and run_debate accept an optional image_url (public HTTPS URL of a JPEG/PNG). One Gemini Flash vision call extracts a description, which every text-only agent reacts to alongside the question. ~$0.001 + ~1-3s overhead, regardless of panel size — useful for testing diagrams, product photos, charts, screenshots, or political imagery.

run_focus_group and run_debate block 5–120s depending on population size — that's a real synthetic-research call running behind the scenes, not a cached response. The MCP server returns a trimmed envelope (sentiment summary + first 5 representative quotes

• billing breakdown). Use get_simulation to pull the full payload when you need every agent's full reasoning.

Live progress

run_focus_group, run_debate, and compare_options stream via SSE under the hood. When your MCP host (Claude Desktop / Cursor / Cline) sends a progressToken with the tool call (the default), the server emits MCP notifications/progress as agents complete — so the host displays "47/200 agents responded" live instead of a blank "running tool..." indicator. Debate runs additionally emit "Round 3/5 starting" / "Round 3 complete" message updates. No client work needed; progress just shows up.

Recommended flow

For non-trivial studies the agent should:

1. list_demographic_presets — if the user didn't pick one, propose one based on the topic.
2. preview_cost — gets a cents+seconds estimate before committing.
3. Confirm with the user — show them the cost estimate and the proposed audience/size.
4. run_focus_group / run_debate — only after confirmation.

Defaults match the platform's calibrated quality bars — Standard Pulse (200 agents) for run_focus_group, the platform-standard debate (30 agents × 5 rounds) for run_debate. Cheaper defaults would silently weaken the output, and the moat is calibrated quality. Free-tier keys hit over_plan_cap on the 200-agent voting default; the agent should retry with population_size=50 or tell the user to upgrade.

Response shapes

Tool responses are mode-aware and field names are stable. Internal QA fields (consistency_score, model routing, harness flags, sampling metadata) are dropped — agents don't need them.

run_focus_group (voting mode):

{
  "id": "...", "status": "complete", "mode": "voting",
  "topic": "...", "demographic_preset": "us_general_population",
  "population_size": 50,
  "sentiment_summary": {
    "positive": 30, "neutral": 12, "negative": 8,
    "positive_pct": 60.0, "neutral_pct": 24.0, "negative_pct": 16.0
  },
  "sample_responses": [
    { "agent_name": "Maria Chen", "age": 34, "occupation": "Software engineer",
      "sentiment": "positive", "reasoning": "..." },
    "..."
  ],
  "total_responses": 50,
  "billing": { "actual_cents": 12, "plan": "free" }
}

If a stance_statement was provided, the envelope additionally carries stance_statement + sentiment_axis: { positive_label, neutral_label, negative_label } so the agent knows whether positive means "agrees" vs. "supports".

run_debate:

{
  "id": "...", "status": "complete", "mode": "debate",
  "topic": "...", "num_rounds": 5, "agent_count": 30,
  "final_consensus_score": 0.72,
  "summary": "Most agents converged toward ...",
  "key_arguments_for": [ "..." ],
  "key_arguments_against": [ "..." ],
  "dissenting_views": [ "..." ],
  "final_round_responses": [
    { "agent_name": "...", "position": "FOR", "reasoning": "...",
      "confidence": 0.8 },
    "..."
  ],
  "position_shifts_count": 8,
  "billing": { "actual_cents": 18, "plan": "free" }
}

get_simulation returns the same envelope as the originating tool but with every response (no 5-quote cap), still with internal QA fields stripped. Use this when the trimmed envelope from run_focus_group / run_debate isn't enough.

Errors

API failures come back as a typed envelope so agents can pattern-match and react. The error field is one of:

error	When	Agent action
auth_error	401, 403	Get a fresh API key
over_plan_cap	400 — population > tier limit	Lower population_size or upgrade
validation_error	400 / 422	Fix the request
insufficient_funds	402 — wallet drained	Top up at top_up_url
rate_limited	429	Sleep and retry (retry_after seconds when available)
quota_exceeded	429 — monthly token quota	Wait for next month or upgrade
simulation_failed	500 — sim crashed mid-run	Inspect sim_id; fall back to a smaller run
server_error	500/502/503/504	Retry with backoff
configuration_error	local — CROWDOS_API_KEY unset	Tell user to fix host config
internal_error	bug in this MCP server	Report at the GitHub issues link

Insufficient-funds responses also carry balance_cents, needed_cents, and top_up_url so the agent can surface a clear upgrade prompt.

Configuration

Env var	Default	Required
CROWDOS_API_KEY	—	yes (except crowd_sample)
CROWDOS_API_BASE_URL	https://api.crowdos.ai	no

Cost

CrowdOS uses a metered wallet. The MCP server returns the actual debit on every successful call inside billing.actual_cents. Free tier ships with $5 of credit; top up at https://crowdos.ai/account/billing once it runs out.

Free-tier monthly quota is 120k tokens (~3 large studies). Pro tier removes the cap.

Programmatic use (without an MCP host)

The server is also a regular Python module:

python -m crowdos_mcp
# stdio MCP server, waits for messages on stdin

Or import and embed:

from crowdos_mcp.server import build_server
server = build_server()
# server is a configured mcp.server.Server instance

Versioning

Follows semver. The MCP tool surface (tool names, input schemas) is stable; additive changes (new tools, new optional fields) ship as minor versions. Removing or renaming a tool is a major version.

Maintainer publish flow

# 1. Bump version in BOTH pyproject.toml and src/crowdos_mcp/__init__.py
# 2. Commit + push
# 3. Publish:
./scripts/publish.sh         # → PyPI
./scripts/publish.sh --test  # → TestPyPI (dry run)

The script verifies the two version numbers agree, runs tests, cleans dist/, builds, and uploads via twine using ~/.pypirc (needs username = __token__ + password = pypi-<token>).

License

MIT.

Issues / questions

https://github.com/bjnagent/crowd/issues