Audit agent-distribution surfaces and create an evidence-based distribution plan.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
π‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)
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.
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.
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.
Use this Streamable HTTP endpoint in any compatible MCP client:
Available tools:
audit_appget_audit_resultcreate_distribution_planget_customer_zero_evidenceDeploy 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.
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.
MIT
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