MCP Discovery – A Developer‑Centric Tool for Uncovering AI Server Capabilities

MCP Discovery is a command‑line utility that interrogates any MCP (Model Context Protocol) server and presents its full set of capabilities—tools, resources, prompts, sampling options—in a human‑readable format. For developers building AI assistants or integrating external services, knowing exactly what an MCP server can do is essential for designing robust workflows and ensuring compatibility. This tool removes the guesswork by automatically querying the server, parsing its discovery endpoint, and rendering the results in multiple formats that fit seamlessly into documentation pipelines.

Why MCP Discovery Matters

When working with AI assistants, developers often need to connect to diverse back‑end services: file systems, databases, third‑party APIs, or custom inference engines. Each service exposes a distinct set of MCP capabilities that dictate how an assistant can interact with it. Manually inspecting these capabilities is tedious and error‑prone, especially as services evolve. MCP Discovery automates this process, giving teams an up‑to‑date snapshot of what each server offers without manual intervention. This is particularly valuable in continuous integration environments where documentation must stay current with the latest server releases.

Core Features and Capabilities

• Terminal Display: The default command streams a neatly formatted table of tools, resources, and other capabilities straight to the console, enabling quick checks during development or debugging sessions.
• Multi‑Format Export: With the subcommand, users can generate Markdown, HTML, plain text, or JSON files. These outputs are ready to be embedded in project READMEs, wikis, or internal knowledge bases.
• Automated Documentation Updates: The subcommand targets existing Markdown or HTML files, inserting capability listings between user‑defined markers. This allows repositories to maintain accurate documentation automatically as servers change.
• Template Flexibility: Built‑in Handlebars templates (, , , ) cover common use cases, while custom templates can be supplied via file or inline string to match unique branding or formatting requirements.
• GitHub Action Integration: A dedicated action wrapper makes it trivial to schedule discovery runs in CI pipelines, ensuring that any new capability additions are reflected in documentation before a release.

Real‑World Use Cases

• API Gateway Documentation: Teams can run MCP Discovery against a gateway that aggregates multiple MCP services, producing a single source of truth for all downstream capabilities.
• Continuous Documentation: Integrate the tool into a GitHub workflow to regenerate README sections whenever a new version of an MCP server is released, eliminating manual copy‑and‑paste.
• Developer Onboarding: New contributors can quickly run the CLI against a test server to understand available tools and resource types, speeding up setup time.
• Compliance Auditing: By exporting capabilities to JSON or Markdown, auditors can verify that the server exposes only approved tools and resources.

Unique Advantages

• Rust‑Powered Performance: Built in Rust, the CLI offers fast execution and low resource usage, making it suitable for large servers or frequent scans in CI environments.
• Hand‑crafted Templates: The use of Handlebars gives developers fine control over output formatting, enabling consistent styling across documentation sites without additional tooling.
• Zero‑Dependency Runtime: The binary is statically linked, requiring no external libraries on the target host—ideal for containerized or serverless deployments.

In summary, MCP Discovery turns the opaque discovery process of MCP servers into a transparent, repeatable, and automatable step. By providing instant visibility into server capabilities and integrating effortlessly with existing documentation workflows, it empowers developers to build more reliable AI assistants and maintain accurate, up‑to‑date technical artifacts.