Zentrix Agentic Workbench MCP Server

@deslito

代码仓库

基本信息

分类

推理

传输方式

stdio

作者

deslito

提交者

Roy Desire Kiiza

关于 Zentrix Agentic Workbench MCP Server

A short summary of what the server does. Example: Modular agentic AI workflows for dataset cleaning and orchestration.

#agentic-ai #workflow-automation#dataset-cleaning

Zentrix Agentic Workbench MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "zentrix": {
      "command": "node",
      "args": [
        "dist/server.js"
      ],
      "env": {
        "PORT": "3000",
        "NODE_ENV": "production",
        "DATASET_PATH": "/app/datasets/training_set.jsonl",
        "KIMI_API_KEY": "your-kimi-api-key",
        "KIMI_MODEL": "kimi-v1",
        "LOCAL_API_URL": "http://localhost:3000",
        "PROD_API_URL": "https://your-service-name.mcp.so"
      },
      "mounts": {
        "../final_datasets": "/app/datasets"
      }
    }
  }
}

Zentrix Agentic Workbench MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Zentrix Agentic Workbench?

Zentrix Agentic Workbench is a comprehensive pipeline for generating a high-quality, balanced dataset for training and evaluating conversational AI models with multi-tool and file-access capabilities. It combines synthetic data generation with the integration and normalization of external datasets from Hugging Face.

How to use Zentrix Agentic Workbench?

Run the full pipeline with python main.py to generate everything from scratch (synthetic data and external downloads) or use python main.py --merge-only to quickly create the training set from existing processed datasets. Additional options include --force, --skip-downloads, and --num-samples. Evaluation is done via evaluate_with_mcp.py with configurable MCP endpoint, API key, and sample limit.

Key features of Zentrix Agentic Workbench

Combines synthetic data with external Hugging Face datasets
Generates multi-tool invocation and file-access dialogues
Validates and normalizes dataset schemas automatically
Outputs a final balanced training set as training_set.jsonl
Supports evaluation using MCP.so for quality metrics
Offers quick merge-only mode for iterative development

Use cases of Zentrix Agentic Workbench

Training conversational AI models that invoke multiple software tools
Building file-access capable dialogue agents from diverse data
Evaluating dataset quality with exact match and tool invocation accuracy

常见问题

What does the final output look like?

The pipeline produces `final_datasets/training_set.jsonl`, a shuffled, balanced dataset containing 100% of records from both multi-tool and file-access tasks.

How do I skip external dataset downloads?

Use the `--skip-downloads` flag when running `main.py` to generate only synthetic data.

Where are the downloaded external datasets stored?

External datasets are downloaded from Hugging Face into the `downloaded_datasets/` directory during a full pipeline run.

What metrics does the evaluation track?

The `evaluate_with_mcp.py` script tracks exact match accuracy, response structure consistency, tool invocation accuracy, and common failure patterns.

What runtimes or dependencies are required?

Python 3 and the packages listed in the project files. The pipeline uses MCP.so for evaluation and requires a valid API key and endpoint URL.

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