"Understanding File Usage" matching MCP servers:

Provides AI assistants with current Ionic Framework v9 and v8 documentation, including component API references, usage examples for Angular, React, Vue, and vanilla JavaScript, and Ionic blog posts. Enables searching docs, reading pages, listing components, retrieving usage examples, and listing blog posts.

Enables AI agents to search IETF RFCs, inspect obsolescence status, list section headings, and read only relevant sections with clean text. It helps agents avoid citing superseded specifications while reducing context usage.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

Gives AI assistants access to current Capacitor documentation, plugin listings, and Ionic blog posts so they can provide accurate answers about Capacitor APIs and CLI usage.

Enables read-only scanning and text extraction from PDF, DOCX, PPTX, SVG, and PNG files in a local folder, providing the raw text to AI models for summarization or analysis without an external LLM API.

MCP server that exposes petal_components schemas and usage examples to AI coding assistants, enabling generation of idiomatic Phoenix LiveView markup.

Fetches up-to-date, version-specific documentation and code examples for any library, preventing outdated or hallucinated API usage in LLM responses.

An unofficial MCP server that gives AI assistants structured access to Italy's Design System .italia, including components, design tokens, usage guidelines, and GitHub issues, updated nightly.

Exposes Radix Primitives' parts, props, and accessibility contracts to AI coding agents, ensuring correct and accessible component usage.

Connects AI agents with Swagger/Knife4j API documentation, enabling real-time retrieval, search, and structured understanding of API interfaces, parameters, and schemas.

Enables AI agents to efficiently process large local and online PDFs through selective extraction of text, images, and metadata. It provides tools for content search and document outline navigation to optimize context window usage.

A personal knowledge hub MCP server that enables LLMs to query structured information about Akib Dabgar's skills, experience, projects, and goals using Dynamic Context Retrieval for efficient context usage.

Enables efficient exploration of OpenAPI specs via markdown, with progressive disclosure of endpoints, schemas, and batch operations to minimize AI context usage.

High-performance code understanding toolkit that enables batch reading of multiple files with dependency context, structural outline extraction with Java annotation awareness, and precise location of classes/methods across large codebases.

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation

Enables AI agents to query a device catalog and search SDK method references through read-only MCP tools, allowing natural-language questions about device capabilities and API usage.

Exposes Levit design system (GDS) metadata to AI coding tools, enabling queries about color tokens and component usage via natural language.

Enables AI tools to create, inspect, update, and deactivate deep links, read analytics, usage, and referral data, manage webhook deliveries, check App Links / Universal Links files, and search bundled documentation.

Enables efficient Wikipedia access through a progressive retrieval strategy that minimizes token usage by searching, summarizing, and fetching only relevant sections.

MCP server providing accurate Unity API documentation to prevent AI agents from hallucinating signatures, namespaces, and deprecated usage.

Token-efficient MCP server for Jira and Confluence that returns lean, flat responses to minimize token usage. Enables issue/comment/transition management, JQL/CQL search, and Confluence page operations.

MCP server that provides AI coding agents automatic access to AGENTS.md documentation from GitHub repositories, enabling understanding of codebase conventions and patterns.

A TypeScript-based server that visualizes project directory structures in Markdown format, automatically documenting file contents with syntax highlighting and supporting customizable exclusion patterns.

Helps AI assistants access shadcn/ui component documentation and examples through a TypeScript-based MCP server that provides reference information for component details, usage examples, and search capabilities.

Enables AI assistants to query accurate, up-to-date documentation for a React component library, including component lists, search, props, allowed values, defaults, and validated JSX usage examples over stdio.

Enables comprehensive local file system operations including searching and reading content from various formats like PDF, Office documents, CSV, and text files. It features automatic character encoding detection and secure path validation for directory access.

Brings Tabler icons, UI components, page layouts, themes, and live documentation into MCP-compatible AI assistants for correct, hallucination-free usage.

Provides AI agents with access to Toss developer documentation and icon catalogs, enabling search and retrieval of official docs and icon usage recommendations.

Enables scoring a local markdown documentation file out of 100 across categories like title, install, usage, license, badges, structure, and relative link resolution, with letter grades and a concrete list of fixes. It works on local files only, providing a heuristic checklist of documentation completeness rather than judging code quality, security, or SEO.

A Model Context Protocol server for Confluence Cloud that aggressively trims API responses to reduce token usage, with features like disk offload and tunable tool surface.