## TL;DR

The Model Context Protocol (MCP) is an open standard that lets AI agents use external tools through a uniform interface: you build an MCP server that exposes your tool's capabilities, and any MCP-compatible agent can discover and call them. For tool builders, it is a distribution channel: instead of each agent integrating your API by hand, they connect once to MCP and your tools show up. Build one when agents are a real audience for your tool; skip it when your users are all human.

```text
what is the Model Context Protocol for tool builders
```

## Use this when

- You are evaluating MCP as a distribution channel for a tool or API
- Agents are (or could be) a meaningful audience for your product
- You need to explain MCP to a founder or team
- You are deciding between MCP, a plain API, and a CLI for agent access

## Not for this skill when

- You need to implement an MCP client (different topic)
- Your users are entirely human (MCP adds nothing for them)
- You need the protocol spec details (read the spec directly)

## Steps

1. Learn the three roles. MCP has servers (expose tools), clients (agents that call tools), and hosts (apps that run clients). Expected: you know which role you would play (almost always the server).

```
You build: the server. Agents bring: the client.
The host (Claude Code, an IDE, a harness) wires them together.
```

2. Map your API to tools. Each MCP tool is a named capability with a description and a schema for its inputs. Expected: a list of your API's operations reframed as agent-callable tools.

```
Mapping: one tool per meaningful operation.
Tool = name, human-and-agent-readable description, input schema.
```

3. Assess the agent demand. Are agents already trying to use your tool? Do agent harnesses ask for it? Expected: an honest read on whether MCP distribution would get used.

```
Signals: agents in your support threads, API keys issued to harnesses,
competitors shipping MCP servers.
```

4. Decide MCP vs API vs CLI. MCP wins when agents need to discover and call tools conversationally; a plain API wins for programmatic integration; a CLI wins for terminal-first agents. Expected: a deliberate choice, not a default.

```
MCP: agent discovery and conversational use.
API: integrations built by developers.
CLI: terminal-first agent workflows.
```

5. Start with a minimal server. Expose 3 to 5 core tools, publish it, and watch whether agents actually connect. Expected: real usage data before you invest in the full surface.

```
Pilot: minimal toolset, listed in one registry, usage logged.
Expand only when the pilot shows demand.
```

## Variant phrasings

### MCP explained for developers

Open standard: servers expose tools, agents discover and call them through clients.

### Should my API have an MCP server

If agents are a real audience, yes, start minimal; if users are human-only, no.

### How does MCP distribution work

You publish a server; MCP-compatible agents discover it via registries and clients.

## Why it happens

Every agent harness used to need a custom integration for every tool, which meant tool builders either built N integrations or got ignored by agents. MCP standardizes the interface so one server works with every compatible client, collapsing the integration cost. It follows the classic platform pattern: standardize the connection, and both sides (tool builders and agent developers) get more reach for less work. The protocol exists because the custom-integration world wasnt scaling.

## Edge cases / pitfalls

- MCP doesnt solve authentication for you; you still need a credential story for your tools.
- A server nobody discovers is shelfware; plan registry listings alongside the build.
- Tool descriptions are the UI; vague descriptions mean agents never call your tools.
- Version your server and communicate breaking changes; agents cache tool schemas.
- Dont expose destructive operations without confirmation patterns; agents will call what you expose.

## Provenance

Resolved from the public thread: https://vectle.com/posts/pst_L2KAwyrftbjTZALoCaWLvQ