Overview

The Jellyfish MCP Server bridges AI assistants with the rich, structured data housed in a Jellyfish instance. By exposing the full Jellyfish API as a set of intuitive tools, it enables developers to ask natural‑language questions about engineering metrics, team composition, and project deliverables without writing raw API calls. This server turns a complex REST interface into a conversational knowledge base that AI agents can query on demand, dramatically lowering the barrier to integrating internal engineering analytics into day‑to‑day workflows.

What Problem Does It Solve?

In many organizations, engineering data lives behind a proprietary API that requires authentication, endpoint discovery, and schema parsing. Developers and product teams often need to surface this information inside chat‑based tools or AI assistants, but writing custom wrappers for every query is time‑consuming and error‑prone. The Jellyfish MCP Server automates this process: it provides a standardized set of tools that map directly to Jellyfish endpoints, handles authentication tokens, and normalizes responses into a format the Model Context Protocol expects. This eliminates repetitive boilerplate code and lets teams focus on building higher‑level logic rather than low‑level API plumbing.

Core Value for AI Developers

For developers building AI‑powered applications, the server offers a single entry point to all of Jellyfish’s data. Once integrated, any AI assistant that supports MCP—such as Claude Desktop or Cursor—can ask questions like “What is the current allocation for the design team?” or “Show me the sprint summary for team X.” The server translates these natural‑language prompts into precise API calls, returning structured JSON that the assistant can render or further process. This tight coupling means AI agents can provide real‑time insights, generate reports, and even trigger workflows without leaving the chat interface.

Key Features & Capabilities

• Comprehensive Tool Set: Tools cover general schema discovery (, ), allocation queries across persons, teams, and categories, delivery details for deliverables, metrics aggregation at company/people/team levels, and search functions for people and teams.
• Unified Authentication: A single Jellyfish API token (and optional PromptGuard token for security) powers all interactions, simplifying credential management.
• PromptGuard Integration: Optional Llama PromptGuard 2 support mitigates prompt‑injection risks, adding an extra layer of safety for sensitive data queries.
• Extensible Design: Each tool maps to a specific Jellyfish endpoint, making it straightforward to add new endpoints or modify existing ones without touching the core MCP logic.

Real‑World Use Cases

1. Engineering Analytics Dashboards – AI assistants can pull live allocation and metric data to answer ad‑hoc questions about resource utilization or velocity.
2. Onboarding & Knowledge Transfer – New hires can ask about team structures, deliverable histories, or open pull requests without navigating internal portals.
3. Product Planning – Product managers can query sprint summaries and team capacities to inform release schedules directly within a chat tool.
4. Operational Monitoring – Ops teams can monitor unlinked pull requests or metric thresholds through conversational alerts, streamlining incident response.

Integration Into AI Workflows

The server plugs into any MCP‑compatible client by exposing a set of tools that the assistant can invoke. Once the tool is called, the server handles request construction, authentication, and response parsing automatically. Developers can then embed these tools into higher‑level prompts or chain them with other MCP services, creating complex reasoning pipelines that combine internal data with external knowledge bases. Because the server adheres to MCP standards, it works seamlessly across different AI platforms, ensuring that teams can adopt the same data source regardless of the underlying assistant.

In summary, the Jellyfish MCP Server turns a sophisticated engineering analytics platform into an accessible conversational resource. By abstracting API complexity, providing robust security options, and offering a rich set of data‑retrieval tools, it empowers developers to build AI assistants that deliver real‑time insights and actionable information directly within their existing workflows.