title: 'The Future of MCP' description: 'Where MCP is headed and how to stay ahead'

The Future of MCP

Model Context Protocol is still in its early days, but it's evolving rapidly. In this final lesson, we'll explore where MCP is headed and how to position yourself for the future.

Current State of MCP

As of early 2025, MCP has:

  • Official SDKs: TypeScript and Python implementations
  • Growing ecosystem: Community-built servers for GitHub, Slack, databases, and more
  • Native support: Claude Desktop and other AI applications
  • Enterprise adoption: Organizations building internal MCP server libraries
  • Open standard: Community-driven development

Emerging Patterns

Composite Servers

Servers that aggregate capabilities from multiple sources:

// A server that combines GitHub, Jira, and Slack
class CompositeDevToolsServer {
  async listTools() {
    return [
      ...await githubTools(),
      ...await jiraTools(),
      ...await slackTools()
    ];
  }
}

This pattern will likely become standardized as "server composition" APIs.

Streaming Responses

While MCP currently uses request-response, streaming is coming:

// Future API (not yet available)
server.setStreamHandler('generate_report', async function* (args) {
  yield { type: 'progress', status: 'Querying database...' };
  const data = await queryDatabase();

  yield { type: 'progress', status: 'Analyzing results...' };
  const analysis = await analyze(data);

  yield { type: 'progress', status: 'Generating charts...' };
  const charts = await generateCharts(analysis);

  yield { type: 'result', data: { analysis, charts } };
});

Bidirectional Tool Calls

Servers calling tools on clients (with permission):

// Server requests client to perform an action
await client.requestToolCall({
  name: 'display_notification',
  arguments: {
    title: 'Data Ready',
    message: 'Your report has been generated'
  }
});

Standardization Efforts

Tool Schemas

Standardized schemas for common tool patterns:

  • CRUD operations: Standard interfaces for create, read, update, delete
  • Search patterns: Consistent parameters for search tools
  • Authentication: Standard OAuth/API key handling
  • Pagination: Consistent pagination across servers

Resource URIs

URI conventions emerging:

database://host/table/record
file://path/to/file
api://service/endpoint
search://index/query

These will likely become formal standards.

Capability Discovery

Enhanced capability negotiation:

{
  "capabilities": {
    "tools": {
      "version": "2.0",
      "features": ["async", "streaming", "batching"]
    },
    "resources": {
      "version": "2.0",
      "features": ["subscriptions", "binary"]
    },
    "prompts": {
      "version": "1.0"
    }
  }
}

Performance Improvements

Binary Protocols

JSON-RPC is human-readable but not the most efficient. Binary protocols are coming:

  • MessagePack: Drop-in JSON replacement
  • Protocol Buffers: Strongly-typed, efficient
  • Custom MCP binary protocol: Optimized for AI workloads

Connection Pooling

For high-throughput scenarios:

class MCPConnectionPool {
  async execute(toolName: string, args: any) {
    const connection = await this.getConnection();
    try {
      return await connection.callTool(toolName, args);
    } finally {
      this.releaseConnection(connection);
    }
  }
}

AI Agent Improvements

Multi-Step Planning

AI agents will get better at planning multi-tool workflows:

User: "Find all bugs assigned to me and update their status"

Agent plan:
1. Call search_issues(assignee=me, type=bug)
2. For each issue:
   a. Call get_issue_details(issue_id)
   b. Call update_issue(issue_id, status=in_progress)
3. Call send_summary_email()

MCP servers will need to support these patterns efficiently.

Tool Learning

AI models will learn which tools work best for specific tasks, improving tool selection over time.

Security Evolution

Fine-Grained Permissions

More granular permission models:

{
  "permissions": {
    "tools": {
      "read_database": {
        "granted": true,
        "constraints": {
          "tables": ["users", "products"],
          "row_limit": 1000
        }
      },
      "delete_records": {
        "granted": false
      }
    }
  }
}

Sandboxing

Stronger isolation for untrusted servers:

  • Process sandboxes: Servers in isolated processes
  • WASM servers: Servers compiled to WebAssembly for safety
  • Containerized execution: Each server in its own container

Interoperability

Cross-Protocol Bridges

MCP servers that bridge to other protocols:

  • GraphQL bridge: Expose GraphQL APIs as MCP tools
  • REST bridge: Automatically generate MCP tools from OpenAPI specs
  • gRPC bridge: Convert gRPC services to MCP

Standard Library Servers

Official servers for common use cases:

  • File system: Standard file operations
  • HTTP client: Generic HTTP requests
  • Database: Generic SQL/NoSQL access
  • Code execution: Safe code evaluation

Community Ecosystem

MCP Marketplace

A central registry for discovering MCP servers:

# Install from marketplace
mcp install github-server
mcp install slack-server

Server Composition DSLs

Domain-specific languages for combining servers:

# mcp-compose.yaml
servers:
  - github
  - jira
  - slack

workflows:
  - name: create-issue-from-pr
    steps:
      - github.get_pr
      - jira.create_issue
      - slack.notify

Getting Ahead

Stay Informed

Build Reusable Servers

Focus on composable, single-purpose servers:

  • Good: A GitHub server, a database server, a Slack server
  • Less good: An "everything" server that does it all

Contribute to Standards

Participate in the community:

  • Share patterns: Document patterns that work
  • Propose standards: Submit RFCs for common use cases
  • Build tools: Create debugging tools, testing frameworks

Think Long-Term

Design for the future:

  • Versioning: Support multiple API versions
  • Backward compatibility: Don't break existing clients
  • Documentation: Write for humans and AI
  • Monitoring: Instrument for observability

Predictions for 2025 and Beyond

2025:

  • MCP becomes standard in AI apps (like REST for web APIs)
  • Hundreds of production MCP servers
  • First MCP conference/hackathons

2026:

  • Binary protocols widely adopted
  • MCP in mobile apps
  • AI agents orchestrating dozens of servers

2027:

  • MCP 2.0 specification
  • Enterprise-wide MCP server platforms
  • AI-first companies built entirely on MCP

Conclusion

You've completed the MCP Mastery course! You now understand:

  • Fundamentals: What MCP is and how it works
  • Building: Creating servers in TypeScript and Python
  • Production: Error handling, observability, testing, deployment
  • Advanced: Multi-server orchestration, custom transports, enterprise integration
  • Future: Where the ecosystem is heading

MCP is enabling a new generation of AI-powered tools. The servers you build today will power the AI applications of tomorrow.

Keep building, keep learning, and welcome to the future of AI infrastructure.

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The Future of MCP - Compass | Nick Treffiletti — MCP, AI Agents & Platform Engineering