Search DevTools

Jump to any tool or page

Deploy to Agents

Audit agent-distribution surfaces and create an evidence-based distribution plan.

saezbaldo0 stars0 forksAI & Agents
View source

Install

mcp_config.json

{
  "mcpServers": {
    "com-deploytoagents-server": {
      "url": "https://deploytoagents.com/mcp",
      "type": "streamable-http"
    }
  }
}

Documentation

Deploy to Agents SDK

Public JavaScript client and registry metadata for the Deploy to Agents remote MCP server.

The remote server is published in the official MCP Registry as com.deploytoagents/server and is served from https://deploytoagents.com/mcp. The JavaScript client and authenticated agent-first CLI are published as deploytoagents@0.4.1 on npm, with an equivalent deploytoagents==0.1.1 client on PyPI.

Agent-first CLI

npx deploytoagents login
npx deploytoagents whoami
npx deploytoagents portfolio --json
npx deploytoagents discovery --json
npx deploytoagents discovery-record --input observation.json --json
npx deploytoagents audit https://example.com --json

The package installs both deploytoagents and the shorter d2a command. Google login uses Authorization Code with PKCE and a temporary loopback callback. On Windows the refresh credential is encrypted for the current OS user with DPAPI; CI can provide a short-lived identity token through DEPLOYTOAGENTS_TOKEN. Discovery Lab can be read or supplied with a JSON observation file (or stdin via --input -). All command results and errors have stable JSON forms for agent use.

{
  "hostname": "example.com",
  "surface": "claude",
  "model": "model label shown by the surface",
  "prompt": "Exact generic, unbranded prompt",
  "outcome": "not-mentioned",
  "freshSession": true,
  "responseExcerpt": "Optional relevant excerpt",
  "citations": ["https://example.org/source"]
}

Valid outcomes are recommended, mentioned, not-mentioned, and error. The server verifies that the signed-in organization owns the target hostname.

Deploy to Agents currently audits public agent-facing surfaces, returns unlisted evidence receipts, and creates prioritized technical and external-authority distribution plans. It does not yet claim to publish every customer artifact or guarantee recommendation by any model.

Connect directly through MCP

Use this Streamable HTTP endpoint in any compatible MCP client:

https://deploytoagents.com/mcp

Available tools:

  • audit_app
  • get_audit_result
  • create_distribution_plan
  • get_customer_zero_evidence

JavaScript client

npm install deploytoagents
import { DeployToAgentsClient } from "deploytoagents";

const client = new DeployToAgentsClient();

try {
  const queued = await client.auditApp("https://example.com");
  console.log(queued.receipt_url);

  const result = await client.getAuditResult(queued.audit_id);
  if (result.status === "completed") {
    console.log(await client.createDistributionPlan(queued.audit_id));
  }
} finally {
  await client.close();
}

Python client

pip install deploytoagents
from deploytoagents import DeployToAgentsClient

async with DeployToAgentsClient() as client:
    queued = await client.audit_app("https://example.com")
    print(queued["receipt_url"])

Customer Zero

Deploy to Agents uses its own system as its first customer. The current 100/100 receipt verifies technical surfaces only. Independent, unbranded discovery remains explicitly not-yet-proven.

Development

npm install
npm test
npm run smoke

Evidence policy

This project distinguishes owned evidence, externally verified artifacts, and independent recommendations. It does not create fake testimonials, automated community posts, coordinated votes, or links intended primarily to manipulate rankings.

License

MIT

Sourced from the repository README.

More in AI & Agents