"Information about bug classification" matching MCP servers:

Enables fast text classification using Model2Vec static embeddings with 10 default categories (technology, business, health, etc.), supports custom category management, batch processing, and provides both local and remote deployment options.

A small MCP server that gives agents rich context about a YouTube video — its transcript, jump-to-the-moment deep links, metadata, and most-replayed moments — so they can answer questions, summarize, pull quotes, or surface highlights.

Lets coding agents such as Claude Code and Codex hand summarising, field extraction, classification and OCR to the free, offline on-device model for Apple Foundation Models on macOS 27. One Rust binary, installed with Homebrew.

An MCP server that provides local, private, synchronous access to Ollama models for prompt answering, text classification, and model listing, without any file access or command execution.

A FastMCP server that enables AI assistants to extract structured information from unstructured text using Google's langextract library through a secure, optimized Model Context Protocol interface.

Generates natural, fluent Chinese content that reads like human writing using the Qwen3-Next-80B-A3B model. Supports web search for latest information and direct file saving with customizable writing rules.

Analyzes YouTube videos using Google's Gemini API, allowing users to get summaries or ask questions about video content via direct URL input.

Routes narrow text-processing tools (classification, extraction, summarization, etc.) to budget models to reduce token consumption on the main model.

Provides MCP tools for querying and summarizing Northwest A&F University campus information, including trending QQ channel posts, official website notices/events/competitions, and cross-site custom searches with Markdown output and source links.

distill-mcp-v2 is a high-performance, network-dependency-free Python FastMCP server designed to aggressively optimize Large Language Model (LLM) context windows. It provides specialized tools for compressing and analyzing massive AI-agent payloads without losing critical semantic information.

MCP server for Glanceway, the macOS menu bar information app, enabling AI assistants to read sources and items.

Enables sentiment analysis of text blocks using the Api Ninjas API, returning sentiment scores and overall sentiment classification for up to 2000 characters of text.

MCP server for Yandex GPT API enabling text generation, embeddings, classification, summarization, and tokenization.

Provides various AI capabilities through DeepInfra's OpenAI-compatible API including image generation, text processing, embeddings, speech recognition, object detection, and classification tasks. Enables users to access multiple AI models for diverse tasks like generating images from prompts, transcribing audio, analyzing text sentiment, and performing computer vision operations.

Enables delegating narrow tasks such as classification, schema-based extraction, and summarization to local models running through Ollama or LM Studio, with code-validated structured outputs.

Unofficial MCP server wrapping crawl4ai that enables extraction and analysis of content from web pages, PDFs, Office documents, YouTube videos, and more, with AI-powered summarization and Google search integration to reduce token usage while preserving key information.

Enables LLMs to retrieve and analyze Steam game reviews, providing access to review statistics, game information, and helping summarize pros and cons of games.

Wraps the gpt-researcher tool as an MCP server, enabling automated web research, information summarization, and structured report generation via natural language queries.

Adds a single MCP tool that lets Codex request local, read-only draft transformations of user-provided text, using a local Ollama model for translation, summarization, rewriting, extraction, and classification.

Enables local structured extraction, classification, and summarization of text with automatic context management and caching, keeping data on your machine.

Exposes a ticket classification tool that categorizes support tickets by severity, category, sentiment, and summary via MCP, using the Anthropic API for classification and packaged as a Claude Desktop extension.

A Model Context Protocol server that enables Claude to fetch, process, and extract information from PDF documents, including LaTeX mathematical equations.

Enables access to 200,000+ machine learning models through the Hugging Face Inference API. Supports text generation, image creation, classification, translation, speech processing, embeddings, and more AI tasks.

MCP server that lets Claude Code delegate mechanical tasks to a local LLM for summarization, classification, extraction, and drafting.

MCP server that delegates mechanical tasks like summarization, classification, extraction, and drafting to a local Llama.cpp LLM, serving as a cost-optimization layer while Claude handles reasoning and quality control.

Enables users to turn household paperwork into answers about upcoming deadlines, warranties, and plain-language explanations, and to draft related messages such as payment extension requests.

Enables AI language models to interact with YouTube content through a standardized interface, providing tools for retrieving video information, transcripts, channel analytics, and trend analysis.

A note management MCP server that enables users to store, retrieve, and summarize personal notes via a custom URI scheme. It provides tools for adding content and prompts for generating brief or detailed summaries of stored information.

Enables semantic search, retrieval, and summarization of Gmail emails with privacy-aware classification and PII sanitization.

Enables intelligent Gmail management, including email triage, newsletter classification, promotional email cleanup, summarization, filter suggestions, draft replies, and audit logging.