Skip to main content
Glama
Zonxpk
by Zonxpk

OpenGPT

ChatGPT Developer Mode drives local OpenHarness tools through a thin Python MCP bridge.

uv run opengpt-connect --root <abs-path> --mode read|write [--tunnel cloudflare|none]

Clone:

git clone --recurse-submodules <this-repo>

OpenHarness is the upstreams/openharness submodule (https://github.com/HKUDS/OpenHarness.git, pinned SHA). See AGENTS.md (wrap): OpenGPT adapters live in packages/chatgpt-mcp.

Paste the printed ChatGPT MCP URL into ChatGPT as a Server URL (Streamable HTTP, auth none).

--tunnel none is loopback/tests only. It does not print a ChatGPT URL.

v1 file tools are jailed to --root. bash is a host-equivalent shell (cwd pinned, host otherwise reachable). Do not treat the whole MCP as jailed. Workspace .env is readable in read mode unless later deny-listed.

Prefer fast_context for exploratory questions, then read_many and apply_changes (up to 20 files per call). Oversized grep / bash / glob / lsp / read_many / fast_context results are written to --root/.opengpt-spill/ and the model gets a head/tail preview plus that path.

v2 optimizes ChatGPT ↔ MCP round-trips, not Python function-call latency. Typical exploration: v1 used 3–6 tool turns; v2 uses 1 fast_context turn plus ChatGPT's semantic decision. Do not treat that as a fixed wall-clock speedup until measured in ChatGPT Web.

The router does not understand code semantically. It uses OpenHarness's local dry-run candidate scoring plus small deterministic intent rules. ChatGPT remains responsible for semantic decisions. fast_context is read-only. On a strong skill-name match it may prepend bundled/project skill text. Trust model: checked-in project skills are treated as trusted workspace instructions, not untrusted third-party content. Do not point --root at an untrusted repo if those skills should be ignored. Heuristic project memory is appended after repo evidence and must not override it. Direct write tools remain write-mode-only. isolated_change requires a clean git working tree and writes only inside the worktree (shared-dir symlinks such as .venv / node_modules are rejected as write targets).

ChatGPT
   │
   │ one MCP call
   ▼
fast_context
   │
   ├─ OpenHarness dry-run candidate routing
   │      no LLM
   │
   └─ deterministic recipe
          │
          ├─ grep
          ├─ read_many
          └─ optional LSP

Recommended tool use:

Exploring an unfamiliar code area:  fast_context
Exact known search:                 grep
Exact known files:                  read_file / read_many
Applying known edits:               apply_changes
Trial edits + verification:         isolated_change
Running verification:               verify_project
Ad-hoc / short shell:               bash
Long test/build/dev server:         long_task

Tools

Read mode: fast_context, read_file, glob, grep, lsp, read_many.

Write mode adds: write_file, edit_file, apply_changes, verify_project, isolated_change, long_task, bash.

route_preview is local-debug only (opengpt-connect --debug-tools). It is not in the production MCP catalog.

Not exposed: agent/plan/image/MCP-client tools, cron, task_create/local_agent, web, and other OpenHarness extras. verify_project / isolated_change / long_task reuse OpenHarness model-free runners.

Related MCP server: codex-web-bridge

How it works

 You ──ask──► ChatGPT Developer Mode
                    │
                    │  Server URL (Streamable HTTP, auth none)
                    ▼
         ┌──────────────────────────┐
         │  Cloudflare quick tunnel │  default --tunnel cloudflare
         │  *.trycloudflare.com     │
         └────────────┬─────────────┘
                      │  HTTPS
                      ▼
         ┌──────────────────────────────────────────────┐
         │  opengpt-connect  (127.0.0.1:8787)           │
         │                                              │
         │  /t/<16-byte-hex>/mcp   POST GET DELETE      │
         │  /health                                     │
         │                                              │
         │  Origin check  →  path token  →  sessions    │
         │  (100 max, 30 min idle)                      │
         └────────────┬─────────────────────────────────┘
                      │  tools/call  1:1
                      ▼
         ┌──────────────────────────────────────────────┐
         │  ToolAdapter                                 │
         │                                              │
         │  small allowlist                             │
         │  OpenHarness: read/grep/glob/lsp/edit/bash   │
         │  + read_many / apply_changes                 │
         │  + spill oversized output to .opengpt-spill  │
         │                                              │
         │  path jail for file/glob/grep/edit           │
         │  SENSITIVE_PATH_PATTERNS extra deny          │
         │  bash: host-equivalent (cwd pin only)        │
         └────────────┬─────────────────────────────────┘
                      │  tool.execute(..., cwd=root)
                      ▼
         ┌──────────────────────────────────────────────┐
         │  OpenHarness tool classes (library)          │
         │  Not exposed: agent, cron, tasks, web, …     │
         └────────────┬─────────────────────────────────┘
                      │
                      ▼
              approved --root workspace

No inner LLM. The bridge does not read ANTHROPIC_API_KEY or OPENAI_API_KEY. ChatGPT is the only model.

--tunnel none skips Cloudflare and serves /mcp on loopback only.

F
license - not found
-
quality - not tested
B
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Servers

View all related MCP servers

Related MCP Connectors

  • An MCP server that gives your AI access to the source code and docs of all public github repos

  • Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer

  • A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…

View all MCP Connectors

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Zonxpk/opengpt'

If you have feedback or need assistance with the MCP directory API, please join our Discord server