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qualityServes as a guardian of development knowledge, providing AI assistants with curated access to latest documentation and best practices.
Last updated -
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87
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TypeScript
MIT License
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Why this server?
This server is designed as a guardian of development knowledge, providing AI assistants with curated access to the latest documentation and best practices, which is directly relevant to the user's request for documentation.
Why this server?
This server provides AI tools with access to Rust documentation from docs.rs, enabling search for crates, documentation, type information, feature flags, version information, and source code.
Why this server?
Enables AI to manage academic literature with structured note-taking and organization, designed for seamless interaction with Claude.
Why this server?
`godoc-mcp` is a Model Context Protocol (MCP) server that provides efficient access to Go documentation which will assist in understanding and using go projects.
Why this server?
A powerful Model Context Protocol framework that extends Cursor IDE with tools for web content retrieval, PDF processing, and Word document parsing allowing latest docuemntation processing.
Why this server?
Enables efficient web search integration with Jina.ai's Search API, offering clean, LLM-optimized content retrieval with support for various content types and configurable caching for programming related topics.
Why this server?
Semantic Scholar API, providing comprehensive access to academic paper data, author information, and citation networks for all programming related topics and more.
Why this server?
Fetches and extracts comprehensive package documentation from multiple programming language ecosystems (JavaScript, Python, Java, etc.) for LLMs like Claude without requiring API keys.
Why this server?
Provides curated documentation access via the Gemini API, enabling users to query and interact with technical docs effectively by overcoming context and search limitations.
Why this server?
Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references.