Zentrix Agentic Workbench MCP Server

@deslito

Basic information

About 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 config

Add this server to your MCP-compatible client using the configuration below.

{
  "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 tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

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

Frequently asked questions

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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