"How to Read the Browser Console" matching MCP servers:

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

Provides read-only MCP tools for searching and asking over private documents via a local RAG service (reed), returning ranked passages with citations while keeping data on the machine.

A basic MCP server setup guide demonstrating how to configure and run Python-based MCP servers with integration examples for Bright Data web scraping and Apify Actors for product data collection.

Enables AI and LLM systems to interact with the Buttondown newsletter service, supporting email drafting, scheduling, analytics retrieval, and list management through a Model Context Protocol interface.

Provides OpenCode with adaptive long-term memory by storing preferences, lessons, and usage history locally in SQLite, enabling AI to remember and adapt to users through context-based learning.

Provides tools to fetch and search news from The Verge's RSS feed, allowing users to get today's news, retrieve random articles from the past week, and search for specific keywords in recent Verge content.

Enables searching and retrieving documentation from crawled documentation sites as an MCP server, allowing coding agents to query real docs instead of relying on training data.

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

A server that enables LLMs like Claude to query AWS DynamoDB databases through natural language requests, supporting table management, data querying, and schema analysis.

Advanced content gap analysis using Query Decomposition and Keyword Fan-Out (Google's patented methodology). Tells you exactly what user queries your content covers - and what it misses. Built on academic research because I needed to understand how AI search engines actually evaluate content.

A server that leverages Cloudflare Browser Rendering to extract and process web content for use as context in LLMs, offering tools for fetching pages, searching documentation, extracting structured content, and summarizing content.

Local-first, read-only MCP server for searching and retrieving cited evidence from archived Telegram chats, including transcripts and media metadata.

Enables Claude Desktop and other AI tools to interact directly with Figma, allowing for powerful AI-assisted design capabilities through natural language commands.

A browser monitoring and interaction tool that enables AI applications to capture and analyze browser data through a Chrome extension via Anthropic's Model Context Protocol.

A tool that creates MCP (Model Context Protocol) servers from OpenAPI/Swagger specifications, enabling AI assistants to interact with your APIs.

Implementation of an MCP server for the RAG Web Browser Actor. This Actor serves as a web browser for large language models (LLMs) and RAG pipelines, similar to a web search in ChatGPT.

Enables LLMs to query documents using semantic search, supporting PDFs, Word, Excel, and more. Organizes documents by topics from folder structure and provides advanced search features like phrase matching and date filtering.

Local MCP server exposing the dek-memory hybrid memory engine over stdio, with optional ChatGPT tools for managing account memories via a browser profile.

Semantic and exact retrieval over 22M Taiwan court judgments with built-in citation guardrails — bundles carry a read-whitelist so downstream models cannot cite judgments whose reasoning was never read. Also provides exact lookup of administrative interpretations with lifecycle status (repealed / superseded / unverified).