Jotsu MCP – A General‑Purpose Model Context Protocol Server

Jotsu MCP is a lightweight, extensible framework that lets developers expose tools, resources, and prompts as an MCP server. It solves the common pain point of integrating external APIs or custom logic into AI assistants that do not yet support MCP natively. By running Jotsu MCP, a team can transform any HTTP endpoint into a first‑class MCP tool that Claude or other models can invoke through the standard syntax, thereby extending the assistant’s capabilities without modifying the model itself.

The server is built around a workflow concept: JSON files that describe nodes, edges, and the MCP servers they interact with. A workflow can chain together multiple tool calls, transform data, and finally surface a result to the user. The framework ships with a command‑line interface that scaffolds new workflows, runs them locally, and logs every action in a structured format. This makes debugging and iteration straightforward: developers can inspect the sequence of calls, see the payloads exchanged with external services, and tweak node definitions on the fly.

Key features of Jotsu MCP include:

• Server‑agnostic tool registration – any MCP server can be added via a simple JSON entry, and the framework will handle authentication, retries, and error propagation automatically.
• Generic nodes – developers can inject custom logic or output handlers into a workflow without needing to implement new MCP types. These nodes simply pass data downstream, making it easy to hook into logging or UI layers.
• Rich metadata support – each node and server can carry arbitrary metadata, enabling fine‑grained control over permissions, rate limits, or user context.
• Extensible CLI – the command line offers workflow initialization, execution, and debugging utilities that integrate with standard Python tooling ( virtual environments, optional dependencies for OpenAI or Anthropic).

In practice, Jotsu MCP shines in scenarios where an AI assistant must access specialized services: querying a database, invoking a proprietary analytics API, or performing domain‑specific transformations. For example, a customer support bot could call an MCP server that wraps a ticketing system, retrieve the latest tickets, and feed them back to the assistant for contextual replies. Similarly, a data science workflow could chain together a model inference tool, a post‑processing script, and a visualization generator—all orchestrated by a single workflow file.

By abstracting tool calls behind the MCP interface, Jotsu MCP enables developers to compose complex AI pipelines with minimal friction. The framework’s declarative workflow definition, combined with robust logging and a flexible node architecture, gives teams the power to iterate quickly while keeping the integration logic cleanly separated from the model’s core behavior.