Data / MCP / Skills

Data for you. Context for your agent.

Official MCP · AI Engineer

Remote MCP URL
https://ai.engineer/mcp

Markdown guide ↗
Run in your terminal
codex mcp add ai-engineer --url https://ai.engineer/mcp

Setup docs

Add the server with the Codex CLI, then start a new agent session.

Codex setup docs ↗

Already have MCP servers configured? Keep them and add the ai-engineer entry.

After connecting, enable AI Engineer’s tools and try: “What AI Engineer conferences are coming up?” Your agent chooses and calls the tools for you. No local server required.

Reading with an agent? Request /data with Accept: text/markdown or text/plain, or fetch /data.md. Browser requests keep this interactive view.

Streamable HTTP · Public data + private CODE applications · No API key

Public collections

Public AI Engineer collections, live row counts, estimated tokens, downloads and sample rows
CollectionWhat’s includedJSONCSVExploreFields & sample
Talks
Recordings, speakers, topics, and summariesJSONCSVExplore
Speakers
Profiles, biographies, affiliations, and talksJSONCSVExplore
Topics
Ideas connecting the conference archiveJSONCSVExplore
Organizations
Organizations, their speakers, and talksJSONCSVExplore
Chapters
Chapter titles and timestamps within talksJSONCSVExplore
Transcript downloads
Index linking to individual transcript filesJSONCSVExplore
How token estimates work

Token counts are estimates, using one token per four serialized JSON characters. Metadata estimates cover each collection download; the transcript estimate covers the linked transcript files rather than only their index and uses published JSON byte sizes where needed. Actual counts vary by model and format. Each download also returns its format-specific estimate in the X-Estimated-Tokens header. CSV exports store lists and nested fields as JSON inside cells.