MCP protocol adoption: six months in, what actually shipped

Anthropic launched the Model Context Protocol in late 2024. Six months later — September 2025 — the protocol has been donated to the Linux Foundation (covered in the 4387-upvote r/ClaudeAI thread), adopted across vendors, and grown a real ecosystem of community-built servers. Here is the state of MCP adoption at six months.

What shipped

The official MCP ecosystem has matured in three layers:

1. Vendor adoption beyond Anthropic. OpenAI Codex CLI supports MCP. Microsoft’s Copilot extensions ship with MCP compatibility. JetBrains AI Assistant added MCP support. The “Anthropic-only protocol” framing of late 2024 no longer holds.

2. Community MCP server proliferation. The semi-official MCP server registry has grown from ~20 servers at launch to several hundred today. The 466-upvote “I spent way too long cataloguing Claude Code tools” thread on r/ClaudeAI tracks the explosion.

3. The Linux Foundation handoff. Anthropic donated MCP to the Linux Foundation in mid-2025. This matters because it answers the “is this a vendor-specific lock-in?” question — MCP is now a real open standard with governance independent of any one company.

What hasn’t shipped (yet)

Six months in, the gaps that remain:

Production-grade auth. MCP servers run locally as subprocesses by default. Running them remotely / over HTTP with proper auth is technically possible but lacks the polish of a mature standard. Enterprise adoption needs this.

Standardised server discovery. No equivalent to npm-search-for-MCP. Discovering servers happens via README files, GitHub stars, and YouTube tutorials. This will improve.

Cross-tool consistency. A given MCP server behaves slightly differently in Claude Code vs Cursor vs Codex CLI. The protocol is the standard; implementations have variance. Expect this to converge through 2026.

Performance / context-cost optimization. Many MCP servers eat context budget aggressively because tool descriptions are verbose. The ecosystem is collectively learning how to write efficient tool definitions.

How adoption breaks down across vendors

Tool	MCP support	Notes
Claude Code	First-class	Anthropic invented MCP — best support
Cursor	Adds MCP-compatible plugins, growing	Better than 6 months ago
Windsurf / Codeium	Working MCP support, smaller catalog	Improving
OpenAI Codex CLI	MCP-compatible	Newer integration, fewer servers tested
Cline / Roo / Kilo Code	Native MCP support	Strong; OSS-friendly
Aider	Limited / experimental	Aider has its own conventions; MCP is bolted on, not core
JetBrains AI Assistant	Recent MCP support	Behind VS Code-family tools

The pattern: tools that come from the “agent” side of the AI coding world (Claude Code, Cline) have the strongest MCP integration. Tools from the “autocomplete” side (Cursor, Copilot) have catching-up to do.

The signature use cases that emerged

What people are actually using MCP for (six months of community reports):

Database introspection and querying — postgres-mcp, mysql-mcp are top-3 most-installed servers. The agent can answer “is there a user table?” or “what tables reference this column?” without you leaving the coding session.

GitHub / GitLab integration — read issues, write comments, create PRs, run CI status checks. Eliminates the open-browser-paste-link loop.

Filesystem beyond project root — work across multiple projects without context-switching the agent.

Web search — Brave Search MCP, Perplexity MCP. Agent can look up “what is the current API signature for X” instead of guessing.

Memory / knowledge graph — persistent across sessions; agent remembers project-specific conventions.

Internal company tools — the killer enterprise case. Companies are building MCP servers for their proprietary APIs / dashboards / CRMs.

The 2313-upvote “Claude Code is a Beast — Tips from 6 Months” thread is the canonical community reference on how power users actually configure MCP setups.

The hidden cost: tool-definition context

The dirty secret of MCP at scale: every tool you wire in eats context budget on every prompt. Tool definitions get included in the system prompt for each conversation turn. A heavy MCP setup with 15+ tools can burn 5-10K tokens per turn before you’ve typed anything.

Best practice that emerged: install only the MCP servers you actually use. The community-driven move from “install everything you might use” to “install only the 5-8 you actively need” took about three months to settle.

Where MCP is heading

Predictions for the next six months (Q4 2025):

• Better discovery / registry tooling — the Linux Foundation governance enables this
• Production auth standardisation — needed for enterprise adoption
• Cross-vendor compatibility tests — formal test suites that ensure servers behave identically across tools
• MCP-as-a-product: companies will ship paid MCP servers (e.g. “your CRM as an MCP server”) as a SaaS offering
• Server bundling: meta-servers that aggregate multiple capabilities under one tool definition to save context

The honest take

Six months in, MCP is the real winner of the AI coding tool race. Not because Claude Code is best (though it is, marginally), but because MCP is now the assumed substrate for agentic coding. New AI coding tools that don’t support MCP look behind the curve. The protocol is the standard.

For engineers, this is good news. Your MCP investment isn’t locked to one vendor. The skills and configurations transfer across tools. The community-built servers keep working as you switch agents.

For the practical “how to use MCP” content, see our MCP servers essentials guide and MCP deep dive. For the broader Claude Code workflow, Claude Code review.