Overview of the Discorevy Local MCP Server Specification

The Discorevy Local MCP Servers specification provides a standardized, file‑based approach for registering and configuring Model Context Protocol (MCP) servers on a developer’s local machine. By placing a simple Markdown file in the directory, developers can expose their server’s capabilities to any MCP‑compatible client—whether it be Claude, ChatGPT, IntelliJ IDEA, or emerging tools. This eliminates the need for bespoke registration scripts or manual API integrations, streamlining the onboarding process for both developers and AI assistants.

Solving a Common Pain Point

Many teams already deploy MCP servers to extend LLMs with domain‑specific data, compute resources, or custom prompts. However, each tool historically required a unique configuration format or manual discovery process. The Discorevy spec addresses this fragmentation by defining one consistent file layout that all MCP clients can parse. As a result, adding a new server is as simple as creating or editing a Markdown file; the client automatically refreshes from disk and makes the server available without any additional code.

What the Server Does

The MCP server described by a spec file acts as an interface layer between an LLM and external resources. It exposes endpoints for:

• Resource discovery (compute, storage, networking)
• Tool execution (custom actions or prompts)
• Prompt templates and sampling strategies
• Health checks to ensure availability

Clients read the Markdown file, transform its human‑readable description into a machine‑processable MCP schema via an LLM, and then use that schema to make authenticated requests. The spec file therefore serves both as documentation for developers and a machine‑readable contract for the AI.

Key Features Explained

• Declarative configuration: All server metadata—name, ID, URL, API version, authentication type—is expressed in plain Markdown, making it readable by humans and parsable by machines.
• Automatic discovery: Clients watch the folder and refresh their registry on a schedule, ensuring new or updated servers become available instantly.
• Security delegation: The spec does not impose security rules; instead, it leaves authentication details (e.g., OAuth2 endpoints) to be defined per server, allowing teams to enforce their own policies.
• Extensibility: The spec supports arbitrary capability lists (compute, storage, networking) and region annotations, enabling complex deployment topologies.
• Metadata enrichment: Tags such as environment, owner, and priority help clients decide when to route a request to a particular server.

Real‑World Use Cases

• Enterprise AI pipelines: A company can expose its internal data lake via an MCP server, allowing Claude to query proprietary datasets without exposing credentials in code.
• Developer productivity tools: IDEs like IntelliJ or Cursor can automatically discover local language servers, code analyzers, or build tools through the spec, providing instant tool integration.
• Hybrid cloud workflows: By listing both on‑premise and cloud MCP servers, developers can instruct an LLM to select the most appropriate compute resource based on region or cost constraints.
• Rapid prototyping: Start‑ups can spin up a local MCP server for testing, then swap to production by updating the Markdown file—no client reconfiguration required.

Integration with AI Workflows

When an LLM receives a user query, the MCP client consults its local registry to find servers that match the request’s context (e.g., a tool named “image‑generator”). The LLM, guided by the Markdown description, generates an MCP request that includes authentication tokens and endpoint URLs. Because the spec file is human‑readable, developers can quickly audit or modify server capabilities without touching code, fostering a smoother collaboration between AI assistants and backend services.

Unique Advantages

• Zero‑code onboarding: Adding or updating an MCP server requires no programming—just a Markdown file.
• Cross‑tool compatibility: All major MCP clients can consume the same spec, ensuring consistent behavior across IDEs and chat platforms.
• Transparency: The Markdown format makes server capabilities visible to anyone inspecting the repository, enhancing security reviews.
• Future‑proof: As new MCP features emerge (e.g., advanced sampling or custom prompts), they can be appended to the spec without breaking existing clients.

In summary, the Discorevy Local MCP Server Specification turns the complex task of registering and managing MCP servers into a lightweight, human‑friendly process. By leveraging Markdown as both documentation and configuration, it bridges the gap between developers’ operational needs and AI assistants’ dynamic request handling—enabling faster, safer, and more flexible AI‑powered workflows.