Waild MCP

What is an MCP server? A plain-English guide from a team that builds one

By the Waild MCP team · July 2026 · We build an SEO MCP server for Claude, so this is written from first-hand experience, with real data throughout.

“MCP server” went from a phrase nobody typed to roughly 60,500 US Google searches a month in about twenty months. We pulled that number ourselves in July 2026, using the same MCP server this guide describes. That loop, an AI assistant calling live tools to fetch real data mid-conversation, is what the Model Context Protocol exists to make routine.

We build an MCP server for SEO work, so this guide is written from the builder's side of the wire: what the protocol does, what happens underneath when Claude calls a tool, where the word “server” misleads people, and the design decisions that separate a useful server from a JSON firehose.

What is an MCP server?

An MCP server is a program that gives an AI assistant access to external tools and data sources through the Model Context Protocol, an open standard introduced by Anthropic in November 2024. It exposes capabilities such as database queries, file access, and live API data in a format any MCP-compatible assistant, such as Claude, can discover and call.

That paragraph is the whole idea. A large language model (LLM) on its own can only work with the text in its context window. It cannot check today's search volumes, read your Postgres schema, or file a ticket in your project tracker. An MCP server supplies the missing hands and eyes. The official documentation at modelcontextprotocol.io compares MCP to a USB-C port for AI applications: one connector standard instead of a custom cable for every device, enabling any assistant to plug into any capability.

The letters stand for Model Context Protocol. Spelling that out matters, because the same acronym floats around Minecraft modding and medicine, and search engines mix the audiences together.

How does an MCP server work?

The easiest way to build a working understanding of the mechanics is to name the three pieces. The host is the AI application you use: Claude Desktop, Claude Code, an IDE, a growing number of other chat apps. The client is the connection the host holds open. The server is the program exposing capabilities. One host can run multiple clients, each pinned to one server — multi-server setups are the norm — which is how users end up with GitHub, Slack, and an SEO toolkit connected to the same assistant.

Client and server speak JSON-RPC 2.0, plain structured messages over a pipe or a network connection, each naming the method it invokes: initialize, tools/list, tools/call. When Claude connects to our server, this happens, in order:

  1. Handshake. The client sends an initialize request; both sides state their protocol version and what they support.
  2. Discovery. The client calls tools/list. The server returns every tool with a name, a description, and a specific JSON schema for its inputs. Discovery is dynamic: the list reflects what the server exposes today, not what a developer wired last quarter.
  3. Reasoning. Those names and descriptions go into the model's context. This is the step most explanations skip, and it decides everything downstream: the tool description is the only manual the assistant gets. Writing good ones is closer to prompt engineering than to API documentation.
  4. Action. When you ask something Claude cannot answer alone, it emits a tools/call request. Ask about keyword demand, for example, and it might call keyword_overview with {"keywords":["ai note taking app"]}.
  5. Grounding. The server runs the real query and returns the result, which lands in the conversation as evidence for the reply.

Tools — in practice, remotely callable functions with typed inputs — are one of three capability types. Servers can also provide resources, readable data such as files, knowledge-base articles, or reference documents addressed by URI, and prompts, reusable workflows a user can invoke on demand.

MCP servers come in two shapes. A local one runs as a small process on your machine and communicates over stdio. A remote one is a web service reachable over streamable HTTP. The protocol is identical either way.

One distinction trips people up: MCP is not RAG. Retrieval-augmented generation pipes documents into the prompt ahead of time; MCP lets the assistant decide mid-conversation to take actions, so retrieval becomes one tool among many rather than the whole architecture. For AI agents, that difference is the whole job description: an agent plans, acts, and reads the results, and MCP is the socket its actions plug into.

What is an MCP server used for? Real examples

Search demand is a decent map of where the ecosystem is. These are US monthly search volumes for specific server names, based on Google Ads data we pulled in July 2026:

  • GitHub MCP server, 8,100 searches: repositories, pull requests, and issues handled from inside the assistant
  • AWS MCP server, 5,400: querying and managing cloud infrastructure
  • Figma MCP server, 3,600: design files turned into implementation context for coding agents
  • Playwright MCP server, 2,400: an AI agent driving a real browser
  • Slack MCP server, 1,900: reading and posting to team channels

The pattern underneath all five: any system with an API can become a set of tools the assistant wields — including the systems of record a business already runs on: databases, file systems, browsers, enterprise platforms, payment systems, analytics suites. Our own server wraps DataForSEO's APIs so a writer working in Claude can check search volumes, parse competitor pages, and grade a draft against the pages that actually rank, all without leaving the conversation.

If you are a developer wondering how to create an MCP server: official SDKs exist for Python, TypeScript, and several other languages, and a minimal working server is a few dozen lines of code. The protocol is the easy part. Designing tools an LLM can use well is the hard part, which is what the design section below is about.

MCP server vs API: what is the difference?

An MCP server usually is a wrapper around one or more APIs, so the honest comparison is “raw API” versus “API dressed for a model.”

Traditional API MCP server
Built for Developers writing code An AI model deciding at runtime
Discovery Documentation, read at development time tools/list, live at connect time
Integration cost Custom code for each app-to-system pairing One protocol, any MCP host
Response shape Everything the endpoint returns Only what the model needs

Discovery is the deep difference. Traditional integrations are wired by hand, one at a time: connect N applications to M systems and you write N×M integrations. With a shared protocol you write N+M. The host learns what a server can do the moment it connects, so giving your assistant a new capability means adding a server, not shipping new application code.

Will MCP replace APIs? No. It consumes them. Something still has to serve the underlying data, and that something remains an API.

Why does response design matter? What we learned building one

Context windows are the scarce resource, and most API responses were never meant to sit in one. A raw SEO data response for a handful of keywords runs to thousands of tokens: nested metadata, twelve months of history per keyword, long strings of null fields. A research session makes dozens of calls. LLMs fed all of that drown, slow down, and cost more per answer.

Three decisions made our server usable in practice:

Compact responses. Every response is reduced to short field tokens before Claude sees it. A keyword row comes back as {"kw":"note taking app","vol":22200,"cpc":9.91,"kd":49}: volume, cost-per-click, ranking difficulty, done. A separate legend tool decodes the tokens on demand, so the compression costs nothing in clarity.

Caching. Every fact fetched from the paid API lands in an edge database. Repeat lookups are answered from cache, free and instantly, and each cached response carries a _cache marker so the assistant knows the provenance of its own evidence. Agents check the same keyword many times across a session; without a cache you pay every time.

Server-side synthesis. The worst integration pattern is making the assistant stitch ten raw responses together itself. One of our tools fetches the search results for a keyword, parses the top-ranking pages, and returns a single writer-ready brief. Another grades a finished draft against the live competition: in one recorded session, a thin 119-word draft scored 16 out of 100, with 42 AI-cliché hits per 1,000 words and “in conclusion” flagged by name. Claude gets a verdict, not a dump.

That server is Waild MCP, a working name for a product still pre-launch. The homepage shows replays of real recorded sessions, and there is a waitlist if you want to know when the doors open. We mention it here because every claim in this section comes from building it, and you should know where a guide's experience comes from.

How do you connect an MCP server to Claude?

For a local server, you add an entry to Claude Desktop's configuration file naming the command to launch; Claude Code does the same with claude mcp add. For a remote server, you paste its URL as a custom connector in the app's settings. After that there is nothing to operate. The tools appear in the assistant's toolkit automatically, and day-to-day interactions don't change: you chat, most users never look at the plumbing again, and Claude decides when a question needs a tool.

Two important, practical notes on security. A remote connection is only as secure as the service behind it: credentials stay on the server side, so the assistant never sees your API keys, and reputable MCP servers authenticate every request over a secure HTTPS connection. Access control sits with the host too — it asks the user for permission before a tool acts on your systems. Connect servers you trust, and read what a specific tool call is about to do before approving it, the same discipline you would apply to any third-party access to your accounts. A secure setup is mostly a matter of choosing well and approving deliberately.

Frequently asked questions

What is the difference between an API and an MCP server?

An API serves data to programs written by developers in advance. An MCP server presents tools to an AI model at runtime, with self-describing names and schemas it reads at connection time. Most MCP servers are thin, model-friendly layers over existing APIs.

Does ChatGPT use MCP?

Yes. OpenAI adopted the protocol in March 2025 for its Agents SDK, with ChatGPT connector support following. Google DeepMind announced MCP support for Gemini shortly after. What began as an Anthropic standard now works across the major AI platforms, which is much of why it matters.

Is an MCP server a real server?

Frequently not in the hardware sense. Many MCP servers run as a small local process on your own machine, communicating with the host over standard input and output. Remote ones are ordinary web services. “Server” names its role in the protocol, the side that serves capabilities, not a rack somewhere.

Why do I need an MCP server?

You need one when your assistant must see live data or act on real systems: current metrics, private databases, internal systems, third-party platforms. If everything relevant already fits in the text you paste into the chat, you do not need one yet.

Are MCP servers replacing APIs?

No. An MCP server is a presentation layer that makes APIs usable by models. The API underneath still does the work; MCP standardizes how an LLM finds it, calls it, and reads the response.

Is MCP still relevant today?

The skeptical essays exist, and the ecosystem is young enough that architectures still shift. The numbers say yes anyway: tens of thousands of monthly searches, adoption across Anthropic, OpenAI, and Google, and thousands of community servers on public registries. Protocols win by being boring and everywhere, and MCP is most of the way there.