Dependency Context – MCP Server Overview

Dependency Context is an MCP (Model Context Protocol) server that gives AI assistants instant, contextual knowledge of the libraries and frameworks your project relies on. By indexing the documentation that ships with each dependency, the server enables semantic search over real‑world API references, tutorials, and best practices. This reduces the need for developers to manually sift through GitHub repos or external docs, allowing assistants to answer questions about a library’s usage patterns, configuration options, and common pitfalls with higher accuracy.

The server solves the problem of context leakage in code‑centric AI tools. When an assistant is asked about how to use or , it typically pulls in generic knowledge from its training data. Dependency Context bridges that gap by providing a local, up‑to‑date knowledge base derived from the exact versions your project uses. Developers can specify which packages to index via a lightweight , ensuring that the assistant focuses on relevant libraries and keeps indexing time minimal.

Key capabilities include:

• InitializeDependencyIndex – Scans a project’s dependency list, clones each package’s repository (or pulls from the registry), extracts Markdown documentation, and generates vector embeddings for semantic search.
• searchDependencyDocs – Executes a query against the indexed docs, optionally narrowing results to a single dependency. The assistant can then surface precise code snippets or configuration examples directly from the source repository.

These tools are exposed through the MCP interface, so any AI client that understands MCP can invoke them without custom integration code. The server is configurable via environment variables, allowing developers to choose the embedding model, chunk sizes, and debug verbosity. The optional speeds up repository access and bypasses rate limits.

Typical use cases include:

• Code reviews: An assistant can pull up the latest express middleware documentation to verify usage patterns.
• Rapid prototyping: Developers ask “How do I set up JWT authentication in express?” and receive a snippet sourced from the official docs.
• On‑boarding: New team members can query library usage without consulting external tutorials, accelerating learning curves.
• Continuous integration: CI pipelines can invoke the indexer to keep documentation in sync with dependency updates, ensuring AI tools always work against the current codebase.

By integrating Dependency Context into an editor or workflow, developers gain a powerful semantic knowledge layer that keeps AI assistance tightly coupled to the actual code they are writing. The result is more reliable, context‑aware suggestions that directly reflect the libraries your project depends on.