"A tool for querying web pages saved in browser bookmarks" matching MCP servers:

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

MCP server that routes natural language requests to structured tool calls using a LoRA-tuned small language model, with built-in validation, retry, and fallback recovery.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

Call OpenAI Codex from Claude Code for independent second opinions, structured code review, and delegated coding tasks through a FastMCP plugin that drives the codex CLI safely.

Provides a no-op 'think' tool that enables Claude to decompose complex problems and enhance reasoning by pausing to organize thoughts.

Enables any MCP client to query DeepSeek's web chat, including its built-in live web search, with no separate search API, provider account, or extra cost. Also supports continuing chat sessions, uploading local files as prompt attachments, and automatic token re-harvesting from a logged-in browser profile.

Enables AI agents to drive a logged-in ChatGPT web session over CDP, including managing projects and chats, sending messages with file attachments, and retrieving long Pro replies.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Enables users to describe their LLM fine-tuning job once and get the cheapest, fastest, and most balanced GPU options across a dozen cloud providers in seconds.

Enables Codex to delegate code reviews, coding tasks, image/video generation, and background investigations to Grok via MCP tools.

A local MCP/CLI tool that uses a Chrome profile with an active ChatGPT web session to submit image generation prompts and return saved images as MCP content.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

This is a Model Context Protocol (MCP) server that allows AI assistants to interact with the OpenCode CLI. It enables AI assistants to leverage multiple AI models through a unified interface, with features like plan mode for structured thinking and extensive model selection.

Enables operating a dedicated signed-in ChatGPT browser session through MCP, including project-scoped conversations, minimized Windows browser operation, manual login recovery, and listing or downloading result files.

A configurable MCP gateway that runs multiple Streamable HTTP MCP servers and exposes all their tools through a single endpoint, enabling tool aggregation and routing for MCP clients.

Enables users to describe an LLM fine-tuning workload and receive ranked, cost- and runtime-optimized GPU recommendations across multiple cloud providers by estimating VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job once and receive the cheapest, fastest, or most balanced GPU hardware options across a dozen cloud providers by calculating required VRAM/FLOPs and aggregating live pricing.

Enables users to describe an LLM fine-tuning job and receive ranked, task-aware GPU recommendations with estimated VRAM/FLOPs, cost, and runtime across more than ten cloud providers.

Enables users to describe an LLM fine-tuning workload once and get task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, and balanced GPU recommendations across multiple cloud providers.

Enables users to describe an LLM fine-tuning job and receive the cheapest, fastest, or most balanced GPU options across multiple cloud providers by calculating required VRAM and FLOPs.

Enables users to describe an LLM fine-tuning job and receive ranked GPU recommendations across a dozen cloud providers based on estimated VRAM/FLOPs, live pricing, cost, and speed. It returns cheapest, fastest, and balanced hardware options without manual comparison.

Enables users to describe an LLM fine-tuning workload once and get ranked, cost- and speed-optimized GPU recommendations across multiple cloud providers by estimating VRAM/FLOPs requirements.

Enables users to describe an LLM fine-tuning job once and get ranked GPU hardware recommendations with estimated VRAM/FLOPs, cost, and runtime across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive VRAM/FLOPs-aware recommendations for the cheapest, fastest, or most balanced GPU options across a dozen cloud providers.

It enables users to describe an LLM fine-tuning workload once and receive task-aware VRAM/FLOPs estimates plus ranked cheapest, fastest, or balanced GPU recommendations across multiple cloud providers.

It enables users to describe an LLM fine-tuning workload once and automatically estimates required VRAM/FLOPs, compares live GPU pricing across cloud providers, and returns ranked cheapest, fastest, or balanced hardware recommendations.

Enables users to describe an LLM fine-tuning workload and receive ranked, task-aware GPU recommendations by estimating required VRAM/FLOPs and comparing live pricing across a dozen cloud providers.

Enables users to describe an LLM fine-tuning job once and receive ranked GPU recommendations across many cloud providers based on estimated VRAM/FLOPs, cost, and runtime.

Enables users to describe a fine-tuning workload and receive ranked GPU cloud recommendations based on required VRAM/FLOPs, cost, and runtime across multiple providers.

Enables users to describe a fine-tuning workload once and receive task-aware VRAM/FLOP estimates plus ranked cheapest, fastest, and balanced GPU options across multiple cloud providers. It can be used from Claude Code/Desktop or CLI/API to identify viable hardware options without manually comparing provider pricing.