When asking an assistant to explain a library, a common requirement is to find documentation for the target version, answer a specific question, and link to the original source. This requires both locating relevant material and giving the assistant a way to retrieve it.
RAG and MCP address these two parts. RAG is a method for retrieval-augmented generation; MCP is a communication protocol for accessing tools and data. They can be used in the same workflow.
Retrieval Determines Which Material to Read
When there is little material, the relevant documents can be provided directly to the model. Once the collection grows into a product manual, multiple versions of API documentation, or a large set of support records, the reading scope needs to be narrowed first.
The basic RAG approach is to organize a searchable collection, retrieve relevant passages when a question arrives, and provide those passages to the model for its answer. Retrieval can use keywords, vectors, or a hybrid approach, with reranking added where useful. A vector database is a common choice, but it is not mandatory for every retrieval workflow.
Retrieval should also preserve relationships between pieces of material. A parameter description may depend on defaults explained in the preceding section, while a code example may apply only to a particular major version. Extracting a few apparently relevant lines can lose these conditions.
MCP Determines How the Assistant Gets the Material
A documentation retrieval service can expose tools through MCP. For example, it might offer tools to find relevant documentation and read selected documents. The assistant searches first and retrieves the original material as needed.
How the service searches internally is a separate decision. It might use full-text search or call an existing RAG system. MCP does not prescribe the database or retrieval model behind the service.
Context7 is a concrete project focused on library documentation and code examples. Its official instructions provide tools to resolve a library name and then query that library's documentation, and recommend specifying the library and version in the question. The useful reference here is the interface design: identify the source collection first, then retrieve material for the task. Providing documentation search does not establish that its private backend uses any particular RAG implementation.
Retrieve Documentation and Current State Separately
Documentation explains how to use an interface. System records describe an object's current state. Many questions require both.
| Question | Material Needed | One Possible Integration Approach |
|---|---|---|
| How does this version handle pagination? | API documentation and examples for the target version | Search documentation, then read the relevant source |
| Has this Issue been closed? | The project's current record | Query the relevant system or API |
| Which fixes are included in this release? | Release notes and related changes | Read release notes together with project records |
All of these capabilities can be exposed as MCP tools without sharing the same retrieval backend. Giving an assistant access to both kinds of material fits the task better than expecting a documentation index alone to answer every question.
A Tool Connection Does Not Guarantee Fresh Material
An MCP interface can return a cache or a static file; a RAG collection can be updated incrementally. Freshness depends on the source, collection time, and update mechanism, not simply on the protocol's name.
For version-sensitive work, include the conditions in the question: the target library version, the runtime environment, and the behavior being investigated. Check which version the returned documentation covers and whether the examples apply to that environment.
A reliable documentation tool should ideally return a title, source link, and version or date information, not just an unattributed passage. Without these fields, the assistant can still summarize the material, but readers have less ability to verify the conclusion.
Start With an Answer That Can Be Checked
A narrowly scoped question is a useful first test. Specify a library and version, ask the assistant to locate the original documentation, and have it identify the passages supporting its conclusion. Check that it found the right source, preserved important conditions, and supplied links that can be followed.
If the material is retrieved but the answer is still wrong, improve retrieval, reading, or reasoning. If the material cannot be obtained at all, address the connection or source coverage. Separating these problems makes it clearer which part needs work.
MCP can connect retrieval services to more assistants. RAG can help an assistant choose what to read from a large collection. The answer delivered to the reader still needs to be supported by original sources and version information.