"How to create Grafana dashboards" matching MCP servers:

Instant web hosting for AI agents. Publish a live site in one call, no account needed.

MCP server that lets you drive Claude Code sessions hands-free by voice from any MCP client, enabling remote models to send prompts, monitor progress, and get results read back.

Enables MCP clients to create, read, message, wait for, interrupt, and settle persistent peer threads through an existing T3 Code server, keeping them visible across its clients. It supports project-scoped coordination with source attribution and retry-safe commands.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

Full AI agent lifecycle platform — create, grade, optimize, deploy and orchestrate AI agents from your IDE or terminal.

Enables routing tasks to the right specialist agents, assembling the needed context from a layered catalog of agents, skills, knowledge, runbooks, topics, tools, and connectors, while remembering how a business wants work done.

Teach any AI agent how you use an MCP server – Soft-Landing to your AI integrations

MCP server that routes Claude Desktop chat into Claude Code, enabling headless execution and terminal handoff with session continuity.

A Python package that implements a Model Context Protocol server for integrating with Flowise API, allowing users to list chatflows, create predictions, and dynamically register tools for Flowise chatflows or assistants.

Governed actions, shared memory, audit, and rollback for MCP clients such as Claude Desktop, Cursor, and Claude Code. Works locally with SQLite or connects to Novyx Cloud for policy-as-code, approval workflows, and governance dashboards.

Connect your Sanity content to AI agents. Create, update, and explore structured content using Claude, Cursor, and VS Code via the Model Context Protocol. Transform content operations from complex queries to simple conversations—giving your team superpowers without sacrificing structure.

A dynamic service that creates and manages Model Context Protocol (MCP) servers, allowing users to spawn, customize, and control multiple MCP servers as child processes.

Enables Claude to use Google Gemini as a secondary AI through MCP for large-scale codebase analysis and complex reasoning tasks. Supports both Gemini Flash and Pro models with specialized functions for general queries and comprehensive code analysis.

An MCP server that provides web search capabilities through the Tavily API and weather data retrieval tools. It demonstrates how to integrate external APIs into AI applications and build agentic workflows with LangGraph.

A Model Context Protocol (MCP) server that demonstrates how to build and implement custom tools for Claude using the mcp-framework.

TypeScript implementation of the Model Context Protocol that standardizes how LLMs connect to data sources and tools, eliminating vendor lock-in and simplifying AI integration.

Enables AI agents to create and manage video compilations through n8n workflows with smart query optimization, quality analysis, batch processing, and trusted channel filtering for platforms like YouTube.

A joke/demo Model Context Protocol server that injects cringe-worthy advertisements into LLM responses, showcasing both how to build MCP servers and the risks of ad-injecting intermediaries.

Enables agents to share whatever they produce as auto-expiring, bearer-accessible links, with tools to create, read, and delete contexts over a no-auth streamable HTTP endpoint.

Enables AI agents to coordinate atomic distributed transactions across a swarm through a prepare/vote phase, unanimous commit-or-abort consensus, quorum validation, and failure recovery. It also layers in vector-clock causal stamping, LWW CRDT reconciliation, and gossip dissemination so multi-node state converges deterministically without external dependencies.

🚀 Tigris MCP Server implements the MCP specification to create a seamless connection between AI agents and Tigris key features like bucket and object management.

Measures and improves how findable, readable, and usable a website is to AI answer engines and autonomous AI agents through hosted AEO audit tools.

Provides a hosted remote MCP server that lets AI agents create and use their own email address to send, receive, verify, and reply to messages.

Connects multiple T3 Code MCP environments through a single authenticated endpoint, giving clients such as ChatGPT one connection with per-instance OAuth authorization and prefixed tool names to keep each environment's tools distinct.

It enables Microsoft Copilot Studio agents to connect to and use MCP-based tools, such as fetching jokes, through an Azure-deployed server and Power Platform connector.

Enables any agent to spawn and manage T3 Code agent threads over HTTP MCP — starting research or coding agents, sending follow-ups, waiting for results, and answering approval prompts. It pairs once as a T3 device, exposes 15 tools, and keeps operations retry-safe.

A sample implementation of a Model Context Protocol server using Next.js and the Vercel MCP Adapter, allowing developers to create AI assistants with custom tools and resources.

Enables AI agents to manage Kanban boards, cards, sessions, and automations through a unified MCP interface with real-time updates, supporting delegation, agent sessions, and human-in-the-loop interactions.

Lets coding agents create and manage threads in a running T3 Code server over MCP.

Enables external clients to read authorized projects and threads and, with passkey-approved leases, perform write operations across multiple explicit environments.