"Generating a module in an iOS Xcode project using MVP with Coordinator architecture" matching MCP servers:

Fetches category data from a Q&A service API and returns a markdown summary grouped by category.

Enables Claude to read and analyze PDF documents with automatic OCR processing for scanned files. Features intelligent text extraction, caching for performance, and secure file access with search capabilities.

Enables LLM clients to search a firm's precedent vault and draft grounded contract clauses, legal memos, and document summaries.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Enables natural language management of Gmail through MCP tools for searching, analyzing, summarizing, drafting, and sending emails, with AI reasoning and user confirmation for actions.

Integrates local language models (like Qwen3-8B) with MCP clients, providing tools for chat, code analysis, text generation, translation, and content summarization using your own hardware.

The Documentize MCP server exposes all document processing capabilities as tools for AI agents and LLM clients — convert, merge, extract, sign, and more, directly from Claude Desktop, VS Code Copilot, Cursor, or any MCP-compatible host.

This MCP server enables AI assistants to perform calculations, fetch live weather, manage in-memory notes, access system info and note resources, and use reusable code review and summarization prompts.

Enables interactive AI chat through the Anthropic API, with document retrieval, slash commands, and extensible MCP tool integrations.

A Model Context Protocol server that implements a simple notes system, allowing users to create, list, and summarize text notes.

A lightweight Gradio application that analyzes text for sentiment (positive/negative) and sarcasm detection using Hugging Face Transformers, designed to run on CPU and compatible with the MCP server architecture.

Enables grounding AI responses in a local document corpus by exposing MCP tools to list, search, and summarize documents, and generating answers using OpenAI.

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.

Model Context Protocol server for Archive of Our Own: search fanfiction and have an AI read and rank fics for you.

MCP server for evidence-based bullet point summarization guidance. Validates and improves bullet lists using scientifically-validated principles from cognitive psychology and UX research.

tooltrim reduces the tokens agents spend re-reading bloated tool results. Run it as an MCP server exposing compress and expand_tool_output, or as a gateway in front of any upstream MCP server: it re-exposes the upstream tools unchanged and shrinks each result (HTML/JSON/logs/tables) before it reaches the model, keeping the relevant content only.

Measures AI-detection risk in text and provides a line-by-line rewrite plan to lower the score.

Search and discover YouTube channels via natural language, track them, and summarize videos without transcripts using Gemini API.

MCP server for koreafilings.com — AI-summarized Korean DART (전자공시) corporate disclosures, paid per call in USDC via the x402 protocol on Base. Tools: get_pricing (free), get_disclosure_summary (0.005 USDC).

Lets any AI agent score and simplify its own text before it reaches a human, using Flesch readability metrics and plain-language rewrites entirely on the local machine.