What is Jellyfish Mcp

Jellyfish MCP is a Model Context Protocol server designed for retrieving and analyzing data from Jellyfish’s API. It enables hosts like Claude Desktop or Cursor to interact with Jellyfish instances, facilitating natural language queries regarding engineering metrics, team data, and other accessible information.

Use cases

Use cases for jellyfish-mcp include generating reports on team performance, analyzing resource allocations by various categories, retrieving metrics for individual engineers or teams, and facilitating communication between different tools and Jellyfish’s API.

How to use

To use jellyfish-mcp, first generate an API token from your Jellyfish instance. Then, utilize the various tools provided by the server to make requests to the Jellyfish API, allowing you to retrieve specific data related to allocations, metrics, teams, and more.

Key features

Key features of jellyfish-mcp include: a comprehensive API schema retrieval, detailed endpoints for allocations, metrics, and team data, and the ability to perform natural language queries to extract relevant information from the Jellyfish API.

Where to use

Jellyfish MCP can be used in software development environments, project management, and engineering teams where data analysis and retrieval from the Jellyfish API are essential for decision-making and performance tracking.

jellyfish-mcp: A Jellyfish MCP Server

Security Notice: There are known risks and inherent limitations in this implementation. Refer to SECURITY.md before using.

Overview

A Model Context Protocol server for retrieving and analyzing data from Jellyfish’s API. This server allows a host (e.g. Claude Desktop or Cursor) to interact with your Jellyfish instance, enabling natural language queries about your engineering metrics, team data, and other information available through the Jellyfish API.

Tools

The server provides several tools for interacting with the Jellyfish API:

General

• get_api_schema - Retrieves the complete API schema with all available endpoints
• list_endpoints - Lists all available API endpoints with their descriptions
• get_endpoint - Makes GET requests to any available API endpoint

Allocations

• allocations_by_person
• allocations_by_team
• allocations_by_investment_category
• allocations_by_investment_category_person
• allocations_by_investment_category_team
• allocations_by_work_category
• allocations_by_work_category_person
• allocations_by_work_category_team
• allocations_filter_fields
• allocations_summary_by_investment_category
• allocations_summary_by_work_category

Delivery

• deliverable_details
• deliverable_scope_and_effort_history
• work_categories
• work_category_contents

Metrics

• company_metrics
• person_metrics
• team_metrics
• team_sprint_summary
• unlinked_pull_requests

People

• list_engineers
• search_people

Teams

• list_teams
• search_teams

Each tool corresponds to a specific Jellyfish API endpoint and allows you to retrieve or search for data as described in the API.

Setup

Jellyfish Setup

1. Generate an API token from your Jellyfish instance
   • Go to the Data Connections page
   • Click the API Export tab
   • Click Generate New Token
   • In the Generate New Token dialog, select a Time To Live value and click Generate. A new token is created and displayed in the dialog
   • Copy the token (you will add it to your host application’s config below)
   • Close the dialog

Local Setup

1. Clone this repository:

git clone [repository-url]
cd jellyfish-mcp

2. Install uv if you haven’t already:

curl -LsSf https://astral.sh/uv/install.sh | sh

3. Restart your terminal or open a new terminal window to ensure uv is in your PATH.

4. Create a virtual environment and install dependencies:

uv venv
source .venv/bin/activate
uv pip install .

Usage

Setup credentials

jellyfish-mcp supports two different modes for setting the Jellyfish Export API token.

1. The preferred method is to use keyring, which comes with jellyfish-mcp. It is
   the most secure and ensures credentials are stored using your operating system’s
   preferred credential store. Set your token on your system by running the following in your shell:

uv run python -m keyring set jellyfish api_token

You will then be prompted to set a password. Paste in your API token from Jellyfish and you’re good to go.
You won’t need to do this again.

2. The other option uses environment variables. If you set JELLYFISH_API_TOKEN it will be used as the credential. Many MCP clients allow passing through environment variables, so refer to your tool’s documentation for best practices. In general this is less secure, and isn’t recommended.

Enable PromptGuard 2

jellyfish-mcp supports using Meta’s Llama PromptGuard 2 model to reduce the likelihood of prompt injections attacks. However, you must manually configure this. To do so:

1. Create an account on https://huggingface.co which is needed download the model.
2. Navigate to the PromptGuard 2 86M model: https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M
3. Access Meta’s terms and request access to Llama models.
4. Wait until you are granted access
5. Create a huggingface API token from https://huggingface.co/settings/tokens (a read-only token is sufficient)
6. Within jellyfish-mcp, run uv run llamafirewall configure and provide the token when prompted. You do not need to have it stored as a git credential.

That’s it. You can safely ignore warnings about TOKENIZERS_PARALLELISM or the Together API key.

Configuration with VSCode + Copilot

1. Open the “Command Palette…”
2. Search for “MCP: Add Server…” and click it
3. In the dialog box that appears provide: </FULL/PATH/TO/u> --directory </ABSOLUTE/PATH/TO/jellyfish-mcp> run server.py
4. Give the MCP server a name
5. Same as part of your user or workspace settings as appropriate.
6. Restart VSCode

Running the Server

When you open VSCode from then on, bringing up the “Chat” window should and turning on “Agent” mode should indicate that many tools have been installed from your MCP server. You can then ask Copilot various questions like:

• “What endpoints are available in the Jellyfish API?”
• “Can you get a list of my organization’s teams?”
• “Show me the API schema”

Configuration with Claude Desktop

1. Create or edit your Claude Desktop configuration file at:

   • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
   • Windows: %AppData%\Claude\claude_desktop_config.json

2. Find your uv installation path by running:

which uv

3. Add the following configuration (replace paths with your values):

{
  "mcpServers": {
    "jellyfish": {
      "command": "/FULL/PATH/TO/uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/jellyfish-mcp",
        "run",
        "server.py"
      ]
    }
  }
}

4. Quit and restart Claude Desktop for the configuration changes to take effect.

Running the Server

The server will start automatically when you open Claude Desktop with the proper configuration. You can then ask Claude questions about your Jellyfish data, such as:

• “What endpoints are available in the Jellyfish API?”
• “Can you get a list of my organization’s teams?”
• “Show me the API schema”

Troubleshooting

If you encounter issues:

1. Check Claude Desktop logs:

tail -n 20 -f ~/Library/Logs/Claude/mcp*.log

2. Verify your API token is correct and has the necessary permissions
3. Ensure your Jellyfish instance is accessible from your machine
4. Check that the paths in your Claude Desktop config are absolute and correct

License

This code is distributed under the MIT license. See: LICENSE.