Best MCP Servers (2026)

1

Playwright MCP

Open-source MCP server that lets LLMs drive real browsers via Playwright and accessibility snapshots.

4.8 (6)

· free

Polna ocenaObišči

Playwright MCP is an open-source Model Context Protocol server that exposes Playwright's browser automation capabilities to large language models. Instead of relying on screenshots and vision models, it surfaces structured accessibility snapshots of web pages, giving agents a fast, deterministic view of the DOM that they can reason over and act on. It lets LLM-powered agents navigate sites, click elements, fill forms, extract data, and run end-to-end workflows across Chromium, Firefox, and WebKit. Because it speaks MCP, it plugs into any compatible client such as Claude Desktop, Cursor, or custom agent frameworks, making real-world browser tasks accessible to autonomous and assisted workflows.

• MCP server interface for LLM agents
• Structured accessibility tree snapshots
• Cross-browser support via Playwright
• Click, type, navigate, and form-filling actions
• Headless or headed browser modes
• Integration with Claude, Cursor, and custom clients

Pydantic AI is an open-source Python framework for building applications powered by large language models. Created by the team behind Pydantic, it brings the same focus on type safety, validation, and developer ergonomics to agent development, making LLM outputs predictable and easier to integrate into production code. The framework supports multiple model providers, structured responses validated through Pydantic models, tool calling, dependency injection, and streaming. It is designed to feel familiar to Python developers and works well alongside existing stacks like FastAPI, making it suitable for everything from quick prototypes to production-grade GenAI services.

• Typed agents with Pydantic-validated outputs
• Support for OpenAI, Anthropic, Gemini, and more
• Tool and function calling with dependency injection
• Streaming responses and async-first design
• Integration with FastAPI and observability tools
• Testing utilities for deterministic agent behavior

Cognee is a memory infrastructure designed for AI agents and LLM-powered applications. It captures interactions, documents, and structured data, then organizes them into a knowledge graph that agents can query for relevant context on demand. Instead of relying solely on prompt stuffing or basic vector search, Cognee combines graph relationships with semantic retrieval so agents can recall facts, reason across past sessions, and improve responses as they accumulate experience. Developers integrate it through a Python SDK and connect it to common LLMs, vector stores, and graph databases.

• Knowledge graph based agent memory
• Semantic and structured data ingestion
• Python SDK for agent integration
• Pluggable LLM and storage providers
• Querying across past sessions and documents
• Self-hosted or managed deployment options

Inbox Zero is an AI-powered email assistant designed to cut down the time you spend managing your inbox. It automatically sorts and prioritizes incoming messages, drafts contextual replies in your voice, and helps you unsubscribe from clutter so important conversations rise to the top. Beyond triage, the tool offers automation rules and bulk actions to clear backlogs quickly. It works alongside your existing email account, learning your patterns over time to suggest smarter responses and routines that keep your inbox under control.

• AI-generated reply drafts
• Automatic email categorization and prioritization
• Bulk unsubscribe from newsletters
• Custom automation rules
• Inbox analytics and cleanup tools
• Smart reminders for unanswered emails

5

Screenpipe

Open-source 24/7 local screen and audio recording for building context-aware AI apps

4.8 (4)

· free

Polna ocenaObišči

Screenpipe is an open-source platform that continuously captures screen activity and audio on your device, storing everything locally so developers can build AI applications grounded in real user context. By indexing what you see, hear, and do, it provides a rich personal data layer that apps and agents can query without sending information to the cloud. The project targets developers creating productivity tools, memory assistants, meeting summarizers, and personalized agents. It exposes APIs and a plugin system so custom pipelines can transform raw recordings into searchable text, transcripts, and structured events that feed downstream LLM workflows. Because all processing happens on the user's machine, Screenpipe emphasizes privacy and ownership of data while remaining extensible through community-built integrations.

• 24/7 screen and audio capture
• Local storage and on-device processing
• OCR and speech-to-text indexing
• Plugin and pipeline architecture
• APIs for querying captured context
• Cross-platform desktop support

6

AgentKit

TypeScript library for building and orchestrating AI agents with tools, memory, and multi-agent workflows.

4.5 (4)

· free

Polna ocenaObišči

AgentKit is an open-source TypeScript framework designed for developers who want to build production-ready AI agents without reinventing core orchestration logic. It provides primitives for defining agents, attaching tools, managing state, and coordinating multi-agent workflows in a type-safe way. The library focuses on composability, letting you chain agents, route tasks between specialists, and integrate with existing model providers and APIs. It fits naturally into Node.js and serverless environments, making it suitable for backend services, internal automation, and customer-facing AI features. Because it is code-first and unopinionated about UI, AgentKit is best suited to engineering teams comfortable working in TypeScript who want fine-grained control over how their agents reason, call tools, and handle long-running tasks.

• Agent and tool abstractions
• Multi-agent routing and handoffs
• State and memory management
• Model-agnostic provider support
• Type-safe APIs for inputs and outputs
• Works in Node.js and serverless runtimes

Bringing the bankless onchain API to MCP

Python tool for converting files and office documents to Markdown.

mcp-clickhouse MCP server

MCP Server for QA Sphere TMS