Model Context Protocol and the Agent–Tool Ecosystem: A Systematic Survey of Standardizing AI Tool Integration

The usefulness of a large language model (LLM) agent is bounded by the tools it can reach. Until recently, every connection between an AI application and an external system—a database, a code repository, a SaaS API, a file system—was a bespoke, one-off integration, producing an N×M fragmentation problem: N applications times M tools, each pair wired by hand. The Model Context Protocol (MCP), introduced by Anthropic in November 2024, proposes to replace this patchwork with a single open standard, explicitly modeled on the Language Server Protocol's success in decoupling editors from language tooling. This paper presents a systematic survey of MCP and the agent–tool ecosystem forming around it. We describe the protocol's architecture—hosts, clients, and servers communicating over JSON-RPC—and its three server primitives (tools, resources, and prompts) together with client-side features such as sampling, roots, and elicitation. We analyze its transport designs (local stdio and remote Streamable HTTP), its evolving OAuth-based authorization model, and the security questions raised by third-party tool servers, including prompt injection through tool descriptions and over-privileged tool exposure. We survey adoption across IDEs, agent frameworks, and model providers; compare MCP with adjacent approaches—native function calling, OpenAPI-described tools, and agent-to-agent protocols such as A2A; and consolidate how tool-using agents are evaluated, from function-calling leaderboards to interactive agent benchmarks. We close with open problems: server trust and registry governance, the absence of a standard MCP-specific benchmark, versioning and capability drift, and the protocol's transition to neutral stewardship under the Agentic AI Foundation.