"How to conduct web searches" matching MCP servers:

A multi-agent research system that decomposes complex queries into targeted sub-questions, searches the web in parallel, scores source credibility, and synthesizes findings into structured markdown reports.

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.

The MCP for the Web Speed Agent SDK that enables post-auth agents.

Enables MCP clients like Codex to delegate coding tasks to DeepSeek Harness, reusing the same Web-visible session for feedback and keeping the conversation in the Harness Web UI.

Enables Codex to delegate coding tasks to an OpenCode CLI locally, returning structured results such as exit codes, session summaries, tool calls, and git diffs.

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.

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.

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.

Enables any MCP client to browse, triage, search, read, and drive pi coding agent sessions through Pi Web's HTTP API via 25 tools. Supports listing and filtering sessions, reading transcripts, flagging conversations awaiting input, sending messages and waiting for replies, inspecting project diffs, and forking, renaming, or deleting sessions over either stdio or Streamable HTTP.

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

Enables MCP hosts to conduct goal-oriented question-selection conversations by starting or resuming sessions, asking pending questions, recording replies or context, adjusting known data, and submitting business feedback through the NBQ API.

Enables learning how to build and use an MCP server with tools, resources, and prompts through a Python example.

An autonomous MCP server that uses the Claude Code CLI to solve coding problems without permission prompts.

One-pass agentic inbox triage as an MCP server: fetch unread Gmail → classify (action_needed/fyi/newsletter/noise) → summarize → extract tasks → draft replies as Gmail DRAFTS (never sends) → flag calendar → write a triage report. Four stdio tools (fetch_emails, save_gmail_draft, append_tasks, write_report); the host is the LLM, so it runs keyless in Claude Code. Gmail scopes: readonly + compose

Exposes web search and page fetching tools via the MCP protocol, allowing integration with AI editors like Cursor for autonomous research workflows.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server for integrating with various LLM clients like Claude Desktop.

Enables AI assistants to interact with Notion workspaces through MCP, supporting database queries, page creation, web searches, and agent workflows with human-in-the-loop task approval.

An MCP server integration that enables Cursor AI to communicate with Figma, allowing users to read designs and modify them programmatically through natural language commands.

Enables users to control the cursor in Figma through verbal commands using an agentic AI agent, streamlining the design process with a new interaction method.

An MCP server that provides a persistent memory layer for AI agents by wrapping the memvid CLI. It enables users to manage memory files, perform hybrid searches, and conduct RAG-based question answering through 40 specialized tools.

A Model Context Protocol server that converts Markdown content to HTML format.

An educational implementation of a Model Context Protocol server that demonstrates how to build a functional MCP server integrating with various LLM clients.

An autonomous academic research and publishing platform that enables AI agents to submit papers, conduct peer reviews, and manage scholarly reputations. It provides a comprehensive suite of tools for manuscript lifecycle management, reproducibility testing, and citation analysis within a purpose-built scholarly ecosystem.

Enables two LLMs to play Tic-Tac-Toe against each other autonomously using a shared tool and an SSE relay. The server facilitates agent-to-agent communication by holding tool responses until the opponent makes a move, managing the game state in real-time.

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.

Enables users to ask questions to a persona named Vincent and get yes/no answers or personalized responses in Vincent's style. Provides a tool to simulate how Vincent would respond to various statements or questions.

An MCP server that interfaces with Google NotebookLM to enable autonomous academic research and systematic knowledge management. It allows users to perform deep web searches and automatically generate study artifacts like research reports, presentation slides, and audio overviews.

Provides seamless integration with Fastly's Next-Gen Web Application Firewall API, enabling AI assistants to manage web application security through natural language interactions.

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

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.