#mcp

Dataset

We evaluated search providers against five open benchmarks covering complementary aspects of agentic search: BrowseComp (hard multi-hop questions that require navigating the live web), Frames (multi-document factoid reasoning), FreshQA (time-sensitive questions where the correct answer depends on recent web information), HLE (Humanity's Last Exam — expert-level academic questions spanning math, science, and humanities), SealQA (ambiguity-robust factoid QA with intentionally misleading snippets), WebWalker (tasks designed around following links across pages to find an answer).

Evaluation methodology

Every task is run through a shared deep-research harness: a single GPT-5.4 agent is given two tools (web search and web fetch) with an iterative budget of up to MAX_TOOL_CALLS=25 tool calls per question. The agent plans sub-queries, fans out searches, fetches specific pages when snippets are insufficient, and returns an answer when it exhausts the number of allowed tool calls or has sufficient information to answer the question. Each answer is then LLM-graded by GPT-5.4. We report accuracy of the final answer.

We measure accuracy and overall cost, which includes LLM token costs and tool call costs.

Testing dates

April 19-21, 2026

The highest accuracy web search for your AI

Why use Parallel Search vs. the default search in Claude?

Parallel runs its own web-scale index (billions of pages, millions added daily) and returns dense, query-relevant excerpts instead of raw HTML or SEO-ranked snippets. On public benchmarks, Parallel outperforms the default search in leading frontier models. Your agent reaches the right answer in fewer round trips and with less wasted context. – https://parallel.ai/blog/free-web-search-mcp

A Kibana MCP server implementation that allows any MCP-compatible client (such as Claude Desktop) to access your Kibana instance via natural language or programmatic requests.

Composable Google Sheets CLI for humans and agents. Read, write, update cells by key—with Agent Skills for Claude Code and OpenAI Codex.

The MCP Inspector is an interactive developer tool for testing and debugging MCP servers. While the Debugging Guide covers the Inspector as part of the overall debugging toolkit, this document provides a detailed exploration of the Inspector’s features and capabilities.

This repository contains a Model Context Protocol server implementation for Reddit that allows AI assistants to access and interact with Reddit content through PRAW (Python Reddit API Wrapper).

Fast, script-friendly CLI for Gmail, Calendar, Chat, Classroom, Drive, Docs, Slides, Sheets, Forms, Apps Script, Contacts, Tasks, People, Groups (Workspace), and Keep (Workspace-only). JSON-first output, multiple accounts, and least-privilege auth built in.

I'm a simple boy, so I like simple things. Agents can run Bash and write code well. Bash and code are composable. So what's simpler than having your agent just invoke CLI tools and write code? This is nothing new. We've all been doing this since the beginning. I'd just like to convince you that in many situations, you don't need or even want an MCP server.

Need to consider this on gpupoet. Would be an interesting experience to track usage and see if it gets used.

We propose a new JavaScript interface that allows web developers to expose their web application functionality as "tools" - JavaScript functions with natural language descriptions and structured schemas that can be invoked by AI agents, browser assistants, and assistive technologies. Web pages that use WebMCP can be thought of as Model Context Protocol (MCP) servers that implement tools in client-side script instead of on the backend. WebMCP enables collaborative workflows where users and agents work together within the same web interface, leveraging existing application logic while maintaining shared context and user control.

There are several advantages to using the web to connect agents to services:

Businesses near-universally already offer their services via the web.

WebMCP allows them to leverage their existing business logic and UI, providing a quick, simple, and incremental way to integrate with agents. They don't have to re-architect their product to fit the API shape of a given agent. This is especially true when the logic is already heavily client-side.

Enables visually rich, cooperative interplay between a user, web page, and agent with shared context.

Users often start with a vague goal which is refined over time. Consider a user browsing for a high-value purchase. The user may prefer to start their journey on a specific page, ask their agent to perform some of the more tedious actions ("find me some options for a dress that's appropriate for a summer wedding, preferably red or orange, short or no sleeves and no embellishments"), and then take back over to browse among the agent-selected options.

Allows authors to serve humans and agents from one source

The human-use web is not going away. Integrating agents into it prevents fragmentation of their service and allows them to keep ownership of their interface, branding and connection with their users.

WebMCP is a proposal for a web API that enables web pages to provide agent-specific paths in their UI. With WebMCP, agent-service interaction takes place via app-controlled UI, providing a shared context available to app, agent, and user. In contrast to backend integrations, WebMCP tools are available to an agent only once it has loaded a page and they execute on the client. Page content and actuation remain available to the agent (and the user) but the agent also has access to tools which it can use to achieve its goal more directly.

Official MCP Registry (modelcontextprotocol.io)

This repository is a collection of reference implementations for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources.

MCP HTTP Wrapper - Expose stdio-based Model Context Protocol servers via HTTP using official Streamable HTTP transport. Supports tools, prompts, resources with JSON-RPC 2.0, SSE streaming, session management & security. Transform any MCP server into a REST API.

MCP provides a standardised “tool directory” so AI can discover and call services using JSON-RPC, without each model having to memorise all the API details.

Rube is a universal MCP server built by Composio. It acts as a bridge between AI assistants and a large ecosystem of tools.

It implements the MCP standard for you, serving as middleware: the AI assistants talk to Rube via MCP and Rube talks to all your apps via pre-built connectors.

schema reference

A2A's Focus: Enabling agents to collaborate within their native modalities, allowing them to communicate as agents (or as users) rather than being constrained to tool-like interactions. This enables complex, multi-turn interactions where agents reason, plan, and delegate tasks to other agents. For example, this facilitates multi-turn interactions, such as those involving negotiation or clarification when placing an order.