Documentation Search MCP Server

Overview

The Document Search MCP Server is a lightweight, Python‑based tool that bridges AI assistants with up‑to‑date documentation from popular machine learning libraries. By leveraging the Serper API for web search and BeautifulSoup for HTML parsing, it retrieves relevant excerpts from LangChain, LlamaIndex, or OpenAI documentation and presents them through the MCP protocol. This eliminates the need for developers to manually sift through web pages, allowing AI agents to query documentation on demand and return concise, context‑rich answers.

Problem Solved

Developers building AI assistants often struggle to keep their models informed with the latest library changes. Documentation updates, new APIs, and deprecations can quickly render hard‑coded knowledge obsolete. The server automates the retrieval of current documentation, ensuring that AI agents can answer questions based on the most recent information without manual intervention.

Core Functionality and Value

• Live Search: Sends queries to Serper, obtaining real‑time search results that reflect the current state of the web.
• Targeted Extraction: Parses returned URLs with BeautifulSoup, extracting only the textual content that is most relevant to the query.
• Library‑Specific Context: Supports dedicated documentation sites for LangChain, LlamaIndex, and OpenAI, allowing precise filtering of search results.
• MCP Integration: Exposes a tool that AI assistants can invoke directly, returning structured text ready for further processing or summarization.

These capabilities make the server a powerful addition to any AI workflow that requires up‑to‑date technical knowledge, such as code generation, debugging assistance, or educational tutoring.

Key Features

• Multi‑Library Support: Seamlessly search documentation across three major libraries.
• HTTP & HTML Parsing: Uses for robust requests and BeautifulSoup for clean text extraction.
• Customizable Timeout & Logging: Provides debug logs and configurable timeouts to handle network variability.
• MCP‑Ready Tools: Includes a lightweight for health checks and the primary tool for documentation retrieval.

Real‑World Use Cases

• Code Generation: An AI assistant can look up the latest LangChain API usage patterns before suggesting code snippets.
• Bug Fixing: Developers can ask the assistant for recent changes in OpenAI’s SDK that might affect authentication flows.
• Learning & Onboarding: New team members can query the assistant for up‑to‑date LlamaIndex examples without consulting external resources.
• Continuous Integration: CI pipelines can invoke the server to verify that code changes remain compatible with current library documentation.

Integration with AI Workflows

The server’s MCP interface allows it to be plugged into any assistant that supports the protocol. A typical workflow involves:

1. The assistant receives a user query about a library feature.
2. It calls the tool with the target library and search term.
3. The server returns a concise excerpt, which the assistant can then summarize or incorporate into its response.

Because the tool operates over HTTP and returns plain text, it can be combined with other MCP tools (e.g., code execution or data retrieval) to build complex, multimodal assistants.

Standout Advantages

• Simplicity: Requires only a Serper API key and minimal dependencies, making deployment quick.
• Real‑Time Accuracy: Unlike static knowledge bases, the server fetches live documentation each time it is queried.
• Extensibility: The architecture allows additional libraries or search providers to be added with minimal changes.
• Developer‑Friendly: Clear logging and error handling help maintain reliability in production environments.

In summary, the Document Search MCP Server equips AI assistants with instant access to authoritative library documentation, enhancing accuracy, reducing developer friction, and enabling richer, more informed interactions.