opendtect-mcp

opendtect-mcp

Model Context Protocol (MCP) server that drives OpendTect headlessly for SEG‑Y import, 3D horizon auto‑tracking, ASCII export, and horizon‑agreement scoring.

Category
访问服务器

README

opendtect-mcp

Model Context Protocol (MCP) server that drives OpendTect headlessly — SEG‑Y import, 3D horizon auto‑tracking, ASCII export, and horizon‑agreement scoring.

It wraps two OpendTect surfaces, both public and documented:

  • odpy (OpendTect's Python framework, Apache‑2.0) for database/survey introspection.
  • the Command Driver (od_main --cmd script.odcmd) for GUI actions (import / track / export).

A pure‑Python scoring layer that turns two tracked horizons into quantitative numbers.

Not affiliated with, endorsed by, or sponsored by dGB Earth Sciences. "OpendTect" is a trademark of dGB Earth Sciences; it is used here only to describe interoperability. This project ships no OpendTect code or data — it invokes a separately‑installed OpendTect via its documented interfaces.


What it exposes (MCP tools)

Tool Needs OD? What it does
survey_info introspection OD install + active survey + readiness check (start here)
list_objects odpy list DB objects of a translator group (Seismic Data, Horizon, …)
import_segy Command Driver import a 3D post‑stack SEG‑Y into the survey
autotrack_horizon Command Driver (experimental, M2) seed + auto‑track a 3D horizon
export_horizon_ascii Command Driver export a tracked horizon to ASCII .xyt
read_horizon no parse an exported horizon (pick count + extent)
compare_horizons no score baseline vs candidate tracking: coverage, holes, RMS pick deviation, verdict

The GUI‑action tools accept dry_run=True to return the generated .odcmd without executing, so you can calibrate menu labels for your OD version safely.


Install

cd opendtect-mcp
pip install -e .            # or: pip install -e ".[dev]" for tests

Python ≥ 3.10. odpy ships with OpendTect; if it isn't on your Python path, pip install odpy or run this server with OpendTect's bundled Python.

Configure

All via env (auto‑detects common install locations otherwise) — see .env.example:

export OPENDTECT_BIN=/path/to/od_main
export OPENDTECT_DATA_ROOT=/path/to/SurveyDataRoot
export OPENDTECT_SURVEY=YourSurvey

Verify:

python -c "from opendtect_mcp.config import ODConfig; import json; print(json.dumps(ODConfig.from_env().check(), indent=2))"

Connect to an MCP client (e.g. Claude Code)

Add to your MCP config (.mcp.json in a project, or the user‑level config):

{
  "mcpServers": {
    "opendtect": {
      "command": "opendtect-mcp",
      "env": {
        "OPENDTECT_BIN": "/path/to/od_main",
        "OPENDTECT_DATA_ROOT": "/path/to/SurveyDataRoot",
        "OPENDTECT_SURVEY": "YourSurvey"
      }
    }
  }
}

Then ask the assistant to call survey_info first.


Status & calibration

  • ✅ Works now, no OD: read_horizon, compare_horizons, all dry_run script generation, the unit tests (pytest).
  • ✅ M0/M1 (introspection + import/export): functional; the .odcmd menu labels are version‑specific and may need one‑time calibration (see odcmd_templates.py, lines marked # CALIBRATE). Use dry_run=True, run against your OD, read odcmdlog.txt, adjust labels.
  • 🚧 M2 (autotrack_horizon): scaffolded; seed placement is the trickiest GUI op to script and needs calibration before dry_run=False.

The Command Driver navigates the GUI by label, so a given OD version only needs calibrating once — keep validated strings in odcmd_templates.py.

See examples/horizon_tracking_comparison.md for the end‑to‑end baseline‑vs‑candidate tracking workflow.

Layout

opendtect_mcp/
  config.py            env-driven OD/survey resolution + readiness check
  command_driver.py    write+run .odcmd, parse odcmdlog.txt
  odcmd_templates.py   parameterized .odcmd builders  (← calibrate here)
  odpy_bridge.py       odpy dbman/common wrappers (degrade gracefully)
  horizon_io.py        ASCII horizon parse + agreement scoring (no OD)
  server.py            FastMCP server + tool definitions
tests/                 no-OD unit tests
examples/              workflow guide

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选