Synthetic Audience MCP

An MCP server for testing whether a draft asset is likely to resonate with a target audience.

Hackathon demo framing:

Input: a blog post, landing page copy, email, pitch, or product announcement.
Audience: a short description of who should care.
Output: predicted resonance, likely audience reactions, objections, and edit suggestions.
Backend today: OpenAI, Anthropic, or Fireworks.
Backend after RFT: swap SYNTH_AUDIENCE_MODEL to the trained Fireworks model.

Why The Hypothesis Is Reasonable

The current RFT artifact trains on verifier-style synthetic audience tasks:

| Evidence | What It Supports | What It Does Not Yet Prove | | --- | --- | --- | | AlignX rows predict which response a profiled user prefers | Audience preference judgement | General writing quality | | BehaviorChain rows predict a persona's next behavior | Persona-conditioned reaction prediction | Full market simulation | | Fireworks RFT reward is binary and measurable | A before/after model comparison can be shown | That every asset critique is automatically better |

So the precise claim for the demo should be:

Fine-tuning on audience/persona verifier tasks can improve structured synthetic-audience judgement signals, which we expose as an MCP tool for draft feedback and iteration.

Avoid overclaiming that it is universally better than GPT/Claude at writing advice. The report should show where it is more specific, preference-aware, and measurable.

Run Locally

Install the small runtime dependency set if the workspace venv does not already have it:

cd synthetic-audience-mcp
../.venv/bin/python -m pip install -r requirements.txt

cd synthetic-audience-mcp
../.venv/bin/python server.py

For LLM-backed feedback, set one provider:

export OPENAI_API_KEY=...
export SYNTH_AUDIENCE_MODEL=gpt-4o-mini

or:

export ANTHROPIC_API_KEY=...
export SYNTH_AUDIENCE_MODEL=claude-3-5-haiku-latest

or Fireworks:

export FIREWORKS_API_KEY=...
export SYNTH_AUDIENCE_PROVIDER=fireworks
export SYNTH_AUDIENCE_MODEL=accounts/ashraymalhotra1-m6wa/models/odysim-verifier-gemma4-rft-20260621

Codex MCP Config

Use codex_mcp_config.example.json as the copy-paste starting point.

{
  "mcpServers": {
    "synthetic-audience": {
      "command": "/absolute/path/to/your/.venv/bin/python",
      "args": [
        "/absolute/path/to/synthetic-audience-mcp/server.py"
      ],
      "env": {
        "SYNTH_AUDIENCE_PROVIDER": "auto"
      }
    }
  }
}

The same command/args/env shape can be used by any MCP client that supports stdio servers.

Remote MCP On Modal

The keyed deployed remote MCP endpoint is:

https://ashraymalhotra1--synthetic-audience-mcp-keyed-mcp-app.modal.run/mcp

The safe no-key connectivity endpoint is:

https://ashraymalhotra1--synthetic-audience-mcp-mcp-app.modal.run/mcp

Codex config shape:

[mcp_servers.synthetic-audience-remote-keyed]
enabled = true
url = "https://ashraymalhotra1--synthetic-audience-mcp-keyed-mcp-app.modal.run/mcp"

This remote endpoint is already tested for MCP connectivity, tool discovery, and OpenAI-backed feedback.

Provider keys are not stored in this repository. For local runs, set them in your shell environment. For Modal runs, attach them through a Modal secret.

Backend Modes

backend="auto": uses SYNTH_AUDIENCE_PROVIDER if set; otherwise OpenAI, Anthropic, then Fireworks.
backend="tuned": uses Fireworks and defaults to accounts/ashraymalhotra1-m6wa/models/odysim-verifier-gemma4-rft-20260621.
backend="openai", backend="anthropic", or backend="fireworks": force one provider.

Optional model overrides:

SYNTH_AUDIENCE_TUNED_MODEL: model for backend="tuned".
SYNTH_AUDIENCE_MODEL: general override when you intentionally pin one provider/model pair.

Tools

synthetic_audience_feedback: predict resonance and give actionable feedback.

Demo Assets

demo_script.md: short hackathon talk track.
samples/blog_post_resonance_demo.md: paste-ready sample asset and audience.

Synthetic Audience MCP