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Gpt2agent

Unclaimed

by robotlearning123

Your codex login → a full ChatGPT Plus/Pro account (every model, deep research, image gen, code exec) inside Claude Code, Codex & any MCP client. One-line install.

Set up this server

01 / Choose your client

02 / Before you connect

Authentication is not specified. Check the project instructions before connecting.

Install the runtime required by the project and make it available to your client.

Project instructions

Use names from the project instructions, separated by commas. Enter names only, never secret values.

03 / Add the configuration

Source: generated from public install instructions

claude mcp add --transport stdio 'gpt2agent' -- 'uvx' 'gpt2agent'

Run in your terminal after replacing any placeholders.

04 / Check it in your client

Open your client’s MCP settings and confirm the server connects and lists its tools. A copied configuration does not confirm a working connection.

agentai-agentchatgptchatgpt-apiclaudeclaude-codecodexdalledeep-researchllmmcpmcp-servermodel-context-protocolopenaipython

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25 of 25 tools

Documented tools (25)

From project documentation. A server handshake does not verify each tool’s description or behavior.

account_status

Plan, country, groups, feature count, subscription expiry

agent

Agent Mode — 262K context with autonomous browsing, code execution, tool use

canvas_execute

Execute code via ChatGPT's Canvas feature (live editing environment)

chat

Talk to any model on your account (gpt-5-3 default, override via model=). Pass gpt-5-5-pro, o3-pro, gpt-5-4-thinking, …

code_interpreter

Run Python in ChatGPT's sandbox. Returns output + any generated charts/images

codex_task_create

Kick off a new Codex task (resolves env from repolabel)

custom_instructions_get

Read your current aboutuser / aboutmodel

custom_instructions_set

Update them (read-modify-write, preserves unspecified fields)

deep_research

Web-augmented research with citations (~30–120 s). Auto-confirms by default

deep_research_heavy

Long-form DR via gpt-5-5-pro + connector (5–30 min, monthly quota). Configurable via [models].heavydr

generate_image

Generate images via ChatGPT's built-in DALL-E. Returns download URLs + metadata

get_conversation

Full message history for a specific conversation (multimodal, code, images)

get_file_download_url

Temporary download URL for a ChatGPT file (~1h expiry)

get_file_info

Metadata for any ChatGPT file (images, uploads)

gpt_chat

Talk through one of your private Custom GPTs (g-p-) — experimental

list_apps

Connected apps + connectors

list_codex_envs

Codex environments (label, repos, network policy)

list_codex_tasks

Recent Codex tasks + status

list_conversations

Recent ChatGPT conversations (titles: emails/phones redacted)

list_custom_gpts

Your private g-p- GPTs

list_models

All models on your account (slug, maxtokens, reasoningtype, capabilities, enabledtools)

list_tasks

Scheduled / completed ChatGPT tasks

memory_create_via_chat

Add a memory (model-initiated workaround — POST /memories is 405)

memory_list

List all ChatGPT memory entries (emails/phones redacted)

memory_search

Keyword filter over memories

Tool change history

Compared across complete checks of the same configuration. Tools were listed, not invoked. Input-schema changes are not measured here.

No complete tool checks yet.

FAQ

Questions about Gpt2agent MCP Server

How do I connect Gpt2agent MCP Server to Claude?
Run `claude mcp add gpt2agent -- uvx gpt2agent` in Claude Code, or add the same command and arguments under mcpServers in Cursor's mcp.json or Claude Desktop's claude_desktop_config.json, then restart the client. The blocks above are ready to paste.
Is Gpt2agent MCP Server free?
The listed licence is MIT. Check the upstream terms for permitted use and commercial requirements; a public repository does not by itself mean the software is free or open source. Connected APIs and hosted services may have separate charges.
What can Gpt2agent MCP Server do?
Gpt2agent MCP Server documents 25 tools to the agent, including account_status, agent, canvas_execute. The descriptions above come from project documentation. A live handshake does not test individual tool behavior.