MCP, Tool Use & How AI Agents Access the Web

AI models generate text. They do not, on their own, search the web, fetch URLs, or interact with browser sessions. Everything an AI agent does beyond text generation happens through tool use — a mechanism where the model outputs a structured request, an external system executes it, and the result is fed back to the model.

Model Context Protocol (MCP) standardizes this mechanism. It is becoming the dominant interface between AI agents and the web data tools they depend on. Understanding MCP and tool use is essential for product leaders whose AI products need live web data.

Tool use: how models interact with the world

Tool use (also called function calling) is the mechanism that lets an LLM invoke external capabilities. The pattern is simple in concept:

The model receives a list of available tools with descriptions and parameter schemas.
During generation, the model decides it needs external information.
Instead of generating text, it outputs a structured tool call — a function name and arguments.
The host application executes the tool call and returns the result.
The model incorporates the result into its response.

When OpenAI introduced function calling in June 2023, it formalized what had been a series of ad hoc prompt engineering techniques. Anthropic, Google, and other model providers followed with their own implementations. The concept is now universal across major model APIs.

For web access, tool use is the bridge. A model cannot send an HTTP request. But it can output {"tool": "search_web", "query": "latest Playwright release notes"}, and the host application can execute that search and return results. The model never touches the network directly — the host mediates every external interaction.

This matters architecturally because it means the choice of web data tools is a host-application decision, not a model decision. The model does not care whether search results come from Exa, Brave Search API, or Tavily. It cares that a search tool is available, that it returns useful results, and that the results fit within its context window.

What MCP is and why it matters

Model Context Protocol is a specification for how AI applications connect to external tools and data sources. Anthropic published it in late 2024 as an open standard. In December 2025, it was donated to the Linux Foundation's Agentic AI Foundation, signaling its transition from a single company's project to an industry standard.

The adoption numbers tell the story: over eight million monthly SDK downloads, more than 5,800 MCP servers, over 300 MCP clients. OpenAI, Google DeepMind, and Microsoft have all adopted MCP in their toolchains. It is, as multiple industry participants have described it, becoming the "USB-C for AI" — a universal connector between agents and tools.

The problem MCP solves

Before MCP, every AI application that needed web access had to write custom integration code for each tool. If you wanted your agent to use Firecrawl for scraping, Exa for search, and Browserbase for browser sessions, you wrote three separate integrations with three different APIs, three different authentication flows, and three different response formats.

MCP standardizes this. An MCP server wraps a tool's API behind a consistent interface. An MCP client (the AI application) connects to any MCP server through the same protocol. Add a new tool by pointing at its MCP server — no custom integration code required.

The specification defines three core primitives:

Tools. Functions the AI model can invoke. A search MCP server might expose search_web and fetch_url tools. A browser MCP server might expose create_session, navigate, and screenshot tools.

Resources. Data the server can provide on demand — similar to a file system or database. An MCP server might expose a set of cached search results or a collection of extracted pages as resources.

Prompts. Pre-built prompt templates that the server provides. A scraping MCP server might include a prompt template for "extract structured data from this URL" that includes the right system instructions for the underlying model.

How it works in practice

A concrete example: a developer uses Claude Code to work on a project. Claude Code connects to a Firecrawl MCP server and an Exa MCP server. The developer asks Claude Code to "check if the Stripe API has changed their webhook verification process."

Claude Code's model determines it needs to search the web.
It invokes the search tool from the Exa MCP server with an appropriate query.
The Exa MCP server calls the Exa API, gets results, and returns them to Claude Code.
The model reads the search results and identifies a relevant Stripe documentation URL.
It invokes the scrape_url tool from the Firecrawl MCP server with that URL.
The Firecrawl MCP server fetches the page, converts it to markdown, and returns the content.
The model reads the documentation and provides an accurate, current answer.

The developer did not write any integration code. They configured two MCP server connections. The model decided when and how to use the tools based on the task.

The MCP ecosystem for web data

Most major web data providers now ship official MCP servers. This is a significant shift — it means these tools are designed to be consumed by AI agents, not just by developers writing scripts.

Search MCP servers

Exa provides an MCP server that exposes its neural search capabilities. An agent can search the web using Exa's embeddings-based retrieval and get back structured results with full content extraction. Tavily's MCP server was one of the most widely adopted before the Nebius acquisition and remains available through the Nebius platform.

Brave Search API has an MCP server that gives agents access to its independent web index. For agents that need search without depending on Google or Bing, this is one of the few options with truly independent data.

Scraping and extraction MCP servers

Firecrawl's MCP server is among the most comprehensive. It exposes tools for scraping individual URLs, crawling entire sites, searching the web, and extracting structured data — all through MCP. An agent can use a single MCP connection to Firecrawl for search, fetch, and extract operations.

Crawl4AI provides an MCP server for its open-source crawler, giving agents access to LLM-friendly web crawling without a commercial API dependency.

Browser MCP servers

Browserbase offers an MCP server for cloud browser sessions. An agent can create a browser session, navigate to a URL, interact with the page, take screenshots, and close the session — all through MCP tool calls. This is how agents handle JavaScript-heavy sites, authenticated pages, and multi-step web workflows.

Playwright's MCP server (maintained by Microsoft) lets agents control local browser instances through MCP. This is useful for development and testing scenarios where a cloud browser service is unnecessary.

The long tail

Beyond the major providers, the MCP ecosystem includes hundreds of community-built servers wrapping various web data sources. There are MCP servers for specific search engines, for RSS feed aggregation, for social media APIs, for government data portals. The breadth of the ecosystem means that whatever web data source an agent needs, there is likely an MCP server for it — or one can be built relatively quickly.

Building vs. using existing MCP integrations

Product leaders face a practical question: should you build custom MCP servers for your specific web data needs, or use existing ones?

When to use existing MCP servers

For standard web access patterns — search the web, fetch a URL, extract content from a page — existing MCP servers from established providers are the right choice. They handle authentication, rate limiting, error handling, and output formatting. They are maintained and updated by the provider. The integration cost is configuration, not development.

If your agent needs to search the web, connect to the Exa or Brave Search API MCP server. If it needs to scrape pages, use Firecrawl's or Crawl4AI's MCP server. If it needs browser sessions, use Browserbase's MCP server. These are solved problems.

When to build custom MCP servers

Custom MCP servers make sense when your data source is proprietary or when existing servers do not expose the specific capabilities you need.

Examples: you have an internal knowledge base that agents need to search. You need to access a partner's API with custom authentication. You want to combine multiple data sources into a single tool that returns pre-processed results. You need domain-specific extraction logic that general-purpose scraping APIs do not handle well.

Building an MCP server is not complex. The specification is well-documented, and SDKs exist in Python, TypeScript, Java, and other languages. A basic MCP server wrapping an existing API can be built in a day. The ongoing cost is maintenance — keeping the server updated as the underlying API changes and as the MCP specification evolves.

The hybrid approach

Most production agent systems use a mix. They connect to established MCP servers for general web access (search, scraping, browsing) and build custom MCP servers for proprietary data sources and domain-specific tools. This minimizes development effort for solved problems while maintaining flexibility for unique requirements.

MCP in the AI agent workflow

Understanding where MCP fits in the broader agent architecture helps clarify its role.

Single-agent systems

The simplest case: one AI model with access to a set of MCP servers. The model receives a user request, decides which tools to use, invokes them through MCP, and synthesizes a response. Claude Code, Cursor, and similar developer tools use this pattern. The model is the orchestrator, and MCP servers are the tools.

Multi-agent systems

In more complex architectures, multiple specialized agents collaborate on a task. A research agent might use search MCP servers. An extraction agent might use scraping MCP servers. A browser agent might use Browserbase's MCP server for interactive tasks. An orchestrator agent coordinates them.

MCP provides the consistent interface that makes this practical. Each agent connects to the MCP servers relevant to its role. Adding a new data source means adding an MCP server connection, not rewriting agent code.

Pipeline systems

Some systems are not fully agentic — they use predefined pipelines with fixed steps. Even in these cases, MCP is useful as a standard interface to web data tools. The pipeline steps can invoke MCP tools rather than calling APIs directly, gaining the benefits of a standard interface without requiring agent-level autonomy.

Current limitations

MCP is young, and product leaders should understand its current limitations.

Authentication complexity. MCP servers that access authenticated web services need credentials. Managing those credentials securely across MCP connections is not fully solved. Each server handles authentication differently, and there is no standard mechanism for credential delegation.

Latency. MCP adds a layer of indirection. A tool call goes from the model to the MCP client, to the MCP server, to the underlying API, and back. For latency-sensitive applications, this overhead matters. It is typically small (tens of milliseconds) but compounds across multiple tool calls in a single response.

Discovery. Finding the right MCP server for a specific need requires browsing directories or reading documentation. There is no standard registry or marketplace. The ecosystem is growing fast enough that discoverability is a real challenge.

Versioning and compatibility. The MCP specification is evolving. Servers built against earlier versions may not work with newer clients, and vice versa. This will stabilize as the specification matures, but early adopters should expect some churn.

The future of agent-web interaction

MCP's trajectory points toward a world where AI agents access the web through a standard interface layer, much as web browsers access websites through HTTP. The agent does not need to know the specifics of each data source — it needs to know that tools are available for search, fetch, extract, and interact.

Several developments will shape this future.

Server aggregation. Rather than connecting to many individual MCP servers, agents will likely connect to aggregator services that provide access to multiple web data tools through a single connection. Some providers are already moving in this direction — Firecrawl's MCP server, for instance, combines search, scraping, and extraction in one server.

Standardized authentication. As MCP matures, expect standard mechanisms for OAuth-based credential delegation. An agent will be able to access authenticated web services using the user's credentials, with proper consent and scoping.

Richer interaction models. Current MCP tools are mostly request-response. Future developments will likely include streaming tools (real-time web monitoring), stateful tools (multi-step browser sessions), and collaborative tools (multiple agents sharing a browser session).

Quality and reliability signals. As the MCP ecosystem grows, agents will need ways to evaluate tool quality — response time, accuracy, freshness of data. Standardized metadata about tool capabilities and performance will become important.

For product leaders building AI products today, MCP is the interface to bet on for web data access. It is backed by every major model provider, adopted by every major web data provider, and governed by a neutral foundation. The specific tools behind the MCP servers will evolve and compete. The interface is converging.