mcpField
D
Deepmiro

Deepmiro

Local
github.com/kakarot-devAI & Agents8Updated 23w ago
Repository

About Deepmiro

Simulate hundreds of AI agents to predict how communities react to events and policies. Upload any document (PDF, Markdown, text) and DeepMiro spawns a diverse swarm of AI agents that debate, share, and form opinions — then delivers a calibrated prediction report. Free and open-s

#mirofish#deep-research#research#predicting

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "deepmiro": {
      "command": "npx",
      "args": [
        "-y",
        "deepmiro-mcp"
      ],
      "env": {
        "DEEPMIRO_API_KEY": "<DEEPMIRO_API_KEY>",
        "MIROFISH_URL": "<MIROFISH_URL>"
      }
    }
  }
}
Run commandnpx -y deepmiro-mcp
Environment variables to set
DEEPMIRO_API_KEYSecretYour DeepMiro API key (get one free at https://deepmiro.org)
MIROFISH_URLOverride for self-hosted engine URL (default: https://api.deepmiro.org)

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmdeepmiro-mcp0.1.2

Tools

9

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
promptstringYesScenario description. E.g. 'How will crypto twitter react to a new ETH ETF rejection?'
presetenumNoSimulation preset: quick (10 agents, 20 rounds), standard (20/40), deep (50/72)quick | standard | deep
agent_countintegerNoOverride agent count
roundsintegerNoOverride simulation rounds
platformenumNoTarget platform(s). Default: bothtwitter | reddit | both
document_idstringNoID of a pre-uploaded document (from upload_document tool). Skips file upload and uses server-side sanitized text.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.