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Bio-OS MCP Server
by GBA-BI

Bio-OS MCP Server

A Model Context Protocol (MCP) based tool and prompt server for Bio-OS that provides workflow management and Docker image building capabilities.

Usage

We recommend using the CLINE extension for VSCode to interact with this MCP tool. There are two deployment options available: standalone installation on your local machine or using Code Server in a Miracle Cloud IES instance. Choose the option that best suits your needs.

Local Installation (Using MCP through local VSCode)

Prerequisites

Bio-OS MCP Server requires the following dependencies:

  1. Install uv (Python package manager):

pip install uv
  1. Install Cromwell (Workflow execution engine):

brew install cromwell

Installation

Clone the Bio-OS MCP Server repository:

git clone https://github.com/GBA-BI/bioos-mcp-server.git

Configuration

Configure the Bio-OS MCP Server script path in CLINE's MCP settings. Replace the placeholders with absolute paths to your installation:

{
  "mcpServers": {
    "bioos": {
      "command": "path/to/uv",
      "args": [
        "--directory",
        "path/to/bioos-mcp-server",
        "run",
        "path/to/bioos-mcp-server/src/bioos_mcp/bioos_mcp_server.py"
      ],
      "env": {
        "PYTHONPATH": "path/to/bioos-mcp-server/src",
        "MIRACLE_ACCESS_KEY": "xxxxxxxxxxxx",
        "MIRACLE_SECRET_KEY": "xxxxxxxxxxxx"
      }
    }
  }
} 

Follow the configuration process shown below. The Bio-OS MCP Server is ready to use when the status turns green. If the connection is unstable, click "Retry Connection":

Since CLINE does not yet support MCP Prompts, copy the contents of bioos-mcp-prompt.md into CLINE's Custom Instructions for optimal experience:

After completing the configuration, you can begin using the Bio-OS MCP Server for development.

Cloud Installation (Using MCP through Code Server in Miracle Cloud)

For Miracle Cloud users, we provide a pre-configured Docker image with all Bio-OS MCP Server dependencies. Follow these steps:

  1. Launch an IES instance using the image: registry-vpc.miracle.ac.cn/infcprelease/iespro:250217

  2. Select "Open with VSCode" to access the development environment:

Configuration in Code Server

  1. Click the CLINE icon in the left sidebar and configure your LLM model credentials:

  2. Navigate to CLINE's MCP configuration page and verify that Bio-OS MCP Server is properly connected. Use "Retry Connection" if needed:

Once configured, you can begin development with Bio-OS MCP Server.

Related MCP server: ESMfold MCP Server

Features

1. Workflow Management

  • Submit and monitor workflows

  • Upload WDL workflows

  • Validate WDL scripts

  • Generate input file templates

2. Docker Image Management

  • Build Docker images

  • Check build status

  • Monitor build progress

  • Retrieve build logs

API Reference

Tools

  1. submit_workflow

    • Function: Submit Bio-OS workflow

    • Parameters:

      • ak: Access Key

      • sk: Secret Key

      • workspace_name: Workspace name

      • workflow_name: Workflow name

      • input_json: Input JSON file path

  2. import_workflow

    • Function: Upload WDL workflow to Bio-OS system

    • Parameters:

      • ak: Access Key

      • sk: Secret Key

      • workspace_name: Workspace name

      • workflow_name: Workflow name

      • workflow_source: WDL file path

      • workflow_desc: Workflow description

  3. validate_wdl

    • Function: Validate WDL workflow script

    • Parameters:

      • wdl_path: WDL file path

  4. generate_inputs

    • Function: Generate WDL input file template

    • Parameters:

      • wdl_path: WDL file path

  5. build_docker_image

    • Function: Build Docker image

    • Parameters:

      • registry: Image registry address

      • namespace_name: Namespace

      • repo_name: Repository name

      • tag: Version tag

      • source_path: Dockerfile or archive path

  6. check_build_status

    • Function: Check Docker image build status

    • Parameters:

      • task_id: Build task ID

Prompts (Not supported in Cline yet)

  1. wdl_development_workflow_prompt

    • Function: Generate complete guidance for WDL workflow development

    • Content:

      • WDL script development steps

      • Task structure guidelines

      • Runtime configuration requirements

      • Docker image preparation guide

  2. wdl_runtime_prompt

    • Function: Generate WDL runtime configuration template

    • Content:

      • Required docker image configuration

      • Memory settings (default: 8 GB)

      • Disk settings (default: 20 GB)

      • CPU settings (default: 4)

  3. workflow_input_prompt

    • Function: Generate workflow input preparation guide

    • Content:

      • Input file preparation steps

      • Parameter validation guidelines

      • File path requirements

  4. workflow_submission_prompt

    • Function: Generate workflow submission template

    • Content:

      • Required credentials (ak/sk)

      • Workspace configuration

      • Input file requirements

  5. docker_build_prompt

    • Function: Generate Docker build process guide

    • Content:

      • Dockerfile generation steps

      • Build configuration requirements

      • Image registry settings

      • Build status monitoring guide

Note: Prompts are currently not supported in Cline environment. These prompts are only available when using the MCP server directly or through Claude Desktop.

✅ Certified by MCPHub

MCPHub Certified
This server is officially certified by MCPHub. Verify certification status on our MCPHub profile page.

Contributing

Issues and Pull Requests are welcome.

License

MIT License

Available Tools

22 tools
build_docker_imageD

构建 Docker 镜像

Inputs: config (required). Returns: result

check_build_statusB

检查 Docker 镜像构建状态

Inputs: task_id (required). Returns: result

check_ies_statusC

查看指定workspace中的指定IES 实例的创建状态

Inputs: cfg (required). Returns: result

check_workflow_import_statusC

查询工作流导入状态

Inputs: config (required). Returns: result

check_workflow_run_statusC

查询工作流运行状态

Inputs: config (required). Returns: result

compose_input_jsonC

根据用户给的数值生成input.json

Inputs: cfg (required). Returns: result

create_iesappC

在指定的workspace中新建一个 IES 实例,用户可在该 IES 实例上进行分析

Inputs: cfg (required). Returns: result

create_workspace_bioosC

Bio-OS 创建新工作空间

Inputs: cfg (required). Returns: result

delete_submissionC

Bio-OS 删除工作流提交

Inputs: cfg (required). Returns: result

export_bioos_workspaceD

Bio-OS 导出工作空间元信息

Inputs: cfg (required). Returns: result

fetch_wdl_from_dockstoreC

从Dockstore下载工作流

Inputs: config (required). Returns: result

generate_inputs_json_template_bioosC

Bio-OS 上已导入 workflow 的 inputs.json 查询,并生成符合的输入参数模板

Inputs: cfg (required). Returns: result

get_docker_image_urlC

获取 Docker 镜像的完整 URL

Inputs: config (required). Returns: result

get_ies_eventsB

查看指定workspace中的指定IES实例的创建日志

Inputs: cfg (required). Returns: result

get_workflow_logsD

获取工作流执行日志

Inputs: config (required). Returns: result

import_workflowC

该工具用于将 WDL 工作流上传到 Bio‑OS,支持上传单个文件或整个目录。

Inputs: config (required). Returns: result

list_workspaceC

列出当前登录环境的工作空间名称与描述

Inputs: config (required). Returns: result

search_dockstoreD

在Dockstore中检索工作流

Inputs: config (required). Returns: result

submit_workflowC

提交并监控 Bio-OS 工作流

Inputs: config (required). Returns: result

upload_dashboard_fileB

上传__dashboard__.md文件到指定工作空间的S3桶

Inputs: cfg (required). Returns: result

validate_wdlB

验证 WDL 文件的语法正确性

Inputs: config (required). Returns: result

validate_workflow_input_jsonC

验证工作流输入 JSON 文件

Inputs: config (required). Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    C2.7/5.0

    Scored across 22 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between 'check_workflow_import_status' and 'check_workflow_run_status' which could cause confusion in monitoring workflows. Additionally, 'compose_input_json' and 'generate_inputs_json_template_bioos' both handle input JSON generation, though their descriptions suggest different contexts (user-provided values vs. template generation). Overall, the tools are well-differentiated, but a few pairs require careful reading to avoid misselection.

    Naming Consistency3/5

    The naming follows a mostly consistent verb_noun pattern (e.g., 'build_docker_image', 'create_workspace_bioos'), but there are deviations such as 'fetch_wdl_from_dockstore' (verb_noun_preposition) and mixed use of underscores with terms like 'Bio-OS' in names (e.g., 'export_bioos_workspace'). While readable, the lack of a strict convention across all tools reduces predictability.

    Tool Count4/5

    With 22 tools, the count is on the higher side but reasonable for a comprehensive bioinformatics platform covering Docker builds, workspace management, workflow handling, and IES instances. It feels slightly heavy but not excessive, as each tool appears to serve a specific function in the domain without obvious redundancy.

    Completeness5/5

    The tool set provides complete coverage for the bioinformatics domain, including CRUD operations for workspaces and IES instances, workflow lifecycle management (import, validate, submit, monitor, delete), and supporting utilities like Docker image handling and input generation. No significant gaps are apparent; agents can perform end-to-end tasks without dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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