NPM Documentation MCP Server

Overview

The Mcp Npm Docs Server is an MCP (Model Context Protocol) server designed to give AI assistants instant, reliable access to the metadata and documentation of any NPM package. By leveraging the npms.io API, it fetches a package’s README content, version information, license details, and other descriptive fields. The server stores these results in a local SQLite cache, dramatically reducing latency for repeated queries and ensuring the assistant can respond quickly even when network conditions are suboptimal. For developers, this means a single tool that can surface up‑to‑date package details without the need to manually browse the NPM registry or parse JSON responses.

At its core, the server exposes a single, well‑documented MCP tool called . The tool accepts a package name and an optional flag to bypass the cache, returning a structured JSON payload that includes fields such as , , , , , , , and . This payload is immediately consumable by an AI assistant, allowing it to answer questions like “What does the package do?” or “Show me the license for .” Because the tool returns a consistent schema, downstream logic—whether it’s a natural‑language summarizer or a code generation engine—can rely on predictable data without having to perform additional validation.

The server’s architecture is intentionally lightweight and modular. Configuration is driven by environment variables, enabling fine‑grained control over cache TTL, database location, and logging verbosity. The use of SQLite means the server can run in environments with limited resources or without external database services, making it suitable for both local development and production deployments. The caching strategy also reduces the load on npms.io, which is valuable for high‑traffic AI assistants that may otherwise hit rate limits.

Typical use cases include:

• Documentation lookup: An AI assistant can fetch and summarize the README of a package on demand, providing developers with quick insights into API usage or installation steps.
• Dependency analysis: Tools that audit project dependencies can query package metadata to determine license compliance or vulnerability status.
• Code completion: An assistant can surface the latest version and deprecation warnings before suggesting imports or usage patterns.
• Educational content: Teaching assistants can retrieve example code snippets from a package’s README to illustrate concepts.

By integrating seamlessly into existing MCP workflows, the server allows developers to augment their AI agents with authoritative package data without writing custom parsers or maintaining separate APIs. Its caching mechanism, clear configuration, and single‑tool focus make it a practical addition to any AI‑powered development environment.