For agents

AgentCV as an MCP server

Your agent can use AgentCV directly from Claude, ChatGPT, Cursor, VS Code or any other MCP client: search the registry of AI agent teams, read a team's public profile, and write and check a team profile before a person registers it. It reads the same public data the pages show, with the same labels — real, curated and illustrative — and the same order.

The endpoint

https://agentcv.ai/mcp
Transport
Streamable HTTP, POST only, stateless (no session)
Protocol
MCP 2026-07-28, and 2025-era clients through the same URL
Sign-in
None. Every tool reads public data only
Writes
None. The server stores nothing and cannot register or edit a team
Limits
120 requests a minute per client; 20 teams per search page; 160 KB per request
Logs
The tool name, the status and the duration of each request. No arguments, IP or headers

Connect your client

In alphabetical order. AgentCV is independent and not affiliated with any of these clients; each entry follows that client's own documentation.

ChatGPT

ChatGPT on the web, where your plan and workspace allow custom MCP servers (settings name and availability vary by plan)

  1. Open ChatGPT Plugins (chatgpt.com/plugins), select the plus button, then “Add custom MCP server”.
  2. Enter a name (AgentCV) and a description.
  3. Under Connection, choose a public endpoint and enter https://agentcv.ai/mcp.
  4. Set authentication to none, review the warning, and create it.

Source: https://developers.openai.com/plugins/deploy/connect-chatgpt

Claude (claude.ai, Claude Desktop)

Your Claude account’s connectors (Team and Enterprise: an owner adds it first)

  1. Open Customize → Connectors, select “+ Add”, then “Add custom connector”.
  2. Name it AgentCV and enter https://agentcv.ai/mcp.
  3. Under Authentication choose “No sign in”, then select Add.

Source: https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp

Claude Code

A terminal (add --scope user to use it in every project)

claude mcp add --transport http agentcv https://agentcv.ai/mcp

Source: https://code.claude.com/docs/en/mcp

Codex

~/.codex/config.toml

[mcp_servers.agentcv]
url = "https://agentcv.ai/mcp"

Source: https://learn.chatgpt.com/codex/extend/mcp

Cursor

~/.cursor/mcp.json (every project) or .cursor/mcp.json (one project)

{
  "mcpServers": {
    "agentcv": {
      "url": "https://agentcv.ai/mcp"
    }
  }
}

Source: https://cursor.com/docs/context/mcp

Gemini CLI

A terminal (or httpUrl under mcpServers in ~/.gemini/settings.json)

gemini mcp add --transport http agentcv https://agentcv.ai/mcp

Source: https://geminicli.com/docs/tools/mcp-server/

VS Code

.vscode/mcp.json in a workspace (or the MCP configuration in your user profile)

{
  "servers": {
    "agentcv": {
      "type": "http",
      "url": "https://agentcv.ai/mcp"
    }
  }
}

Source: https://code.visualstudio.com/docs/agents/reference/mcp-configuration

Other clients

Any client that connects to remote MCP servers over Streamable HTTP: the URL is https://agentcv.ai/mcp, with no authentication and no headers. A client that only starts local (stdio) servers can reach it through a stdio-to-HTTP bridge.

The tools

Every tool is read-only and is marked so (readOnlyHint).

search_teams
Search the public AgentCV registry of AI agent teams. Returns public teams only, in the site’s default order: operated teams first, then reference designs (designs documented from papers, framework docs or vendor material that no one is known to run), then clearly labelled illustrative demos. Each team has its layer (real = registered by its owner, curated = documented by AgentCV from cited public sources, illustrative = demo), size wording, platform, topology, trust tier and page URL. All filters are optional and combine with AND. Pages of up to 20; pass nextCursor back as cursor for the next page. Read-only.
get_team
The public profile of one AgentCV team, as its page shows it: layer and its notice (curated teams are documented by AgentCV from public sources and are not endorsed by the team), the reference-design label, owner, trust tier, topology, size, roster (name, role and the model where stated), how it is built and overseen, evidence entries with their links and titles, cited sources, and — for an unclaimed curated team — the links where its operator can claim it or ask for its removal. Only public fields. Read-only.
list_use_cases
The use-case hubs of agentcv.ai/use-cases (code review, testing and QA, research and analysis, …) with how many public teams each holds, by layer (real, curated, illustrative) and by class (operated, reference design, illustrative). Read-only.
list_platforms
The platform hubs of agentcv.ai/platforms (Claude, OpenAI Codex, OpenClaw, CrewAI, LangGraph, …) with the platform names each covers and how many public teams each holds, by layer and by class. AgentCV is independent and not affiliated with any platform. Read-only.
get_registration_schema
The agentcv.team.v1 format for registering a team: the JSON Schema (as an object, and as the exact text agentcv.ai/agentcv-team.schema.json serves) and the self-contained prompt from agentcv.ai/register/with-agent that tells an agent how to write the profile. Registration itself is done by a person on agentcv.ai; this server cannot submit anything. Read-only.
validate_team_profile
Check an agentcv.team.v1 team profile with the same validator the registration page’s import panel uses (the same field rules the server enforces), and with AgentCV’s secret and personal-data detectors. Returns the errors, the warnings (example values left in, values over the form’s limit, ignored keys) and the fields that look like an API key, token, password or personal data (named by path, never repeated), plus the URL where a person signs in, imports the file, reviews it and submits. It stores nothing and submits nothing. Read-only.

Registering a team stays with a person

The server cannot submit anything. An agent writes the team's profile in the agentcv.team.v1 format and checks it with validate_team_profile; then a person signs in, imports the file on the registration form, reviews every field and submits. Values that look like a key, a password or personal data are named in the check's result and never repeated.