How do I use Local Workspace Orchestrator?

This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.

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Local Workspace Orchestrator

by aniketmehetre

Overview Schema Source Related Servers Discussions Admin

Python

Local

Local Workspace Orchestrator

An MCP (Model Context Protocol) client-server system that connects Anthropic Claude to your local filesystem through a set of workspace tools. The orchestrator lets Claude read files, analyse CSVs, execute scripts, generate plots, and more — all from an interactive chat interface.

Architecture

┌───────────────────────────┐        stdio         ┌──────────────────────────┐
│  orchestrator_client.py   │ ◄──────────────────► │  workspace_server.py     │
│  (MCP Client + Anthropic) │     MCP protocol     │  (FastMCP Server)        │
│                           │                      │                          │
│  • Connects to 1+ servers │                      │  Tools:                  │
│  • Streams Claude output  │                      │   • list_workspace_files │
│  • Retries failed calls   │                      │   • summarize_csv_dataset│
│  • Saves chat history     │                      │   • execute_python_script│
│                           │                      │   • write_file           │
│                           │                      │   • run_shell_command    │
│                           │                      │   • plot_column_distrib. │
│                           │                      │                          │
│                           │                      │  Resources:              │
│                           │                      │   • workspace://files    │
│                           │                      │   • workspace://schema/* │
└───────────────────────────┘                      └──────────────────────────┘

Related MCP server: ai-distiller-mcp

Quick Start

1. Clone & install

git clone <your-repo-url>
cd local-workspace-orchestrator

# Using uv (recommended)
uv sync

# Or using pip
pip install -r requirements.txt

2. Set up your API key

cp .env.example .env
# Edit .env and paste your Anthropic API key

3. Run the orchestrator

# Using uv
uv run orchestrator_client.py

# Or directly
python orchestrator_client.py

You'll see the interactive prompt:

======================================================
   Local Workspace Orchestrator Active
   Type queries, or /help for commands, 'quit' to exit.
======================================================

Orchestrator >

4. Try some queries

Orchestrator > list all files in this workspace
Orchestrator > summarize the sample_consumer.csv dataset
Orchestrator > plot the distribution of SpendingScore in sample_consumer.csv
Orchestrator > run the run_analysis.py script

Server Configuration

The orchestrator reads server_config.json to know which MCP servers to launch. The format uses the standard MCP mcpServers structure:

{
  "mcpServers": {
    "workspace_orchestrator": {
      "command": "uv",
      "args": ["run", "workspace_server.py"]
    }
  }
}

Adding more servers

You can connect multiple servers — each will have its tools auto-discovered and registered:

{
  "mcpServers": {
    "workspace_orchestrator": {
      "command": "uv",
      "args": ["run", "workspace_server.py"]
    },
    "my_other_server": {
      "command": "python",
      "args": ["other_server.py"]
    }
  }
}

Chat Commands

Command	Description
`/tools`	List all registered tools by server
`/save [filename]`	Save conversation history to JSON file
`/load [filename]`	Load a saved conversation
`/reconnect <server>`	Reconnect to a dropped server
`/history`	Show conversation message count
`/clear`	Clear conversation history
`/help`	Show all available commands
`quit`	Exit the orchestrator

Available Tools

Read-only tools

Tool	Description
`list_workspace_files`	List files and subdirectories in a workspace path
`summarize_csv_dataset`	Return shape, columns, dtypes, and summary statistics for a CSV
`run_shell_command`	Execute an allowlisted shell command (ls, cat, grep, etc.)

Destructive tools

Tool	Description
`write_file`	Create or overwrite a file in the workspace
`execute_python_script`	Run a Python script and return stdout/stderr
`plot_column_distribution`	Generate a histogram PNG for a CSV column

Resources

URI	Description
`workspace://files`	Lists all files in the workspace root
`workspace://schema/{file_name}`	Column names + dtypes for a CSV file

Security

Path traversal protection: All file-accepting tools validate paths using os.path.realpath() + pathlib.Path.resolve() to prevent directory traversal attacks.
Shell command allowlist: run_shell_command only permits a curated set of read-only commands (ls, cat, grep, head, tail, etc.).
Script sandboxing: execute_python_script runs scripts in a subprocess with a 30-second timeout, restricted to the workspace directory via cwd. Note: this is not a true sandbox — the subprocess has the same OS permissions as the server process.
Tool annotations: Each tool carries readOnlyHint / destructiveHint annotations so MCP clients can reason about safety.

CLI Options

python orchestrator_client.py --help

options:
  --log-level {DEBUG,INFO,WARNING,ERROR}   Set logging verbosity (default: INFO)
  --system-prompt TEXT                     Custom system prompt for Claude
  --config PATH                            Path to server_config.json

Environment Variables

Variable	Description	Default
`ANTHROPIC_API_KEY`	Your Anthropic API key (required)	—
`LOG_LEVEL`	Logging verbosity	`INFO`

Project Structure

local-workspace-orchestrator/
├── orchestrator_client.py    # MCP client + Anthropic integration
├── workspace_server.py       # FastMCP server with workspace tools
├── server_config.json        # MCP server connection configuration
├── main.py                   # Stub entry point
├── run_analysis.py           # Example analysis script
├── sample_consumer.csv       # Sample dataset
├── pyproject.toml            # Project metadata + dependencies
├── requirements.txt          # Pinned pip dependencies
├── .env.example              # API key template
├── .gitignore                # Git ignore rules
└── README.md                 # This file

License

MIT

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

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Local Workspace Orchestrator

Architecture

Quick Start

1. Clone & install

2. Set up your API key

3. Run the orchestrator

4. Try some queries

Server Configuration

Adding more servers

Chat Commands

Available Tools

Read-only tools

Destructive tools

Resources

Security

CLI Options

Environment Variables

Project Structure

License

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Claude Desktop Commander MCP

ai-distiller-mcp

chatgpt-codex-tools-mcp

Terminal MCP Server