[Feature]: Add FastMCP Integration for Model Context Protocol Support

[Harmeet10000]

Prerequisites

• I have searched the existing issues to avoid duplicates
• I understand that this is just a suggestion and might not be implemented

Problem Statement

Currently, the project lacks a standardized way to expose application functionality to AI agents and LLM-powered tools. As AI-driven development workflows become more prevalent, there's no mechanism for:

• AI coding assistants to discover and invoke backend capabilities
• External MCP clients to interact with the application's services
• Controlled integration with third-party MCP servers
• Secure, rate-limited access to application tools via the Model Context Protocol

This limits the project's ability to participate in the emerging AI tooling ecosystem with external ecosystem and prevents seamless integration with modern AI development workflows.

Proposed Solution

Integrate FastMCP to provide a production-ready Model Context Protocol (MCP) server implementation with the following capabilities:

Core Features

1. Dual-Mode MCP Server

   • Local stdio transport for developer tools and IDE integrations
   • Remote HTTP transport mounted into the existing FastAPI application
   • Single codebase supporting both deployment modes

2. Curated Tool Registry

   • Expose safe, read-only tools initially (health checks, metadata, search, fetch)
   • Centralized tool registration to maintain explicit control over the API surface
   • Pagination and response size limiting for production safety

3. Production Security

   • JWT bearer authentication for remote HTTP access
   • Redis-backed rate limiting per subject and endpoint
   • Request logging with correlation IDs
   • Prometheus metrics integration

4. Internal MCP Client Manager

   • Connect to approved upstream MCP servers
   • Support HTTP and stdio upstream transports
   • Tool allowlisting and circuit breaker patterns
   • Graceful connection lifecycle management

Implementation Approach

• Add MCP server under src/app/shared/mcp/
• Reuse existing infrastructure (Redis, Prometheus, logging, JWT auth)
• Mount HTTP endpoint at configurable path (e.g., /mcp)
• Combine FastMCP lifespan with existing FastAPI lifespan
• Add comprehensive settings for server and client configuration

Configuration

Environment-based configuration for:

• Server enable/disable flags
• Transport modes (stdio/HTTP)
• Authentication requirements
• Rate limiting parameters
• Upstream server allowlist
• Tool enablement controls

Alternatives Considered

1. Custom MCP Implementation

   • More control but significantly more development effort
   • Would need to reimplement protocol handling, transport layers, and tooling
   • FastMCP provides battle-tested implementation with FastAPI integration

2. Direct REST API Exposure

   • Doesn't provide MCP protocol compatibility
   • AI tools increasingly expect MCP for standardized discovery and invocation
   • Lacks the structured tool/resource/prompt model MCP provides

3. Third-Party MCP Proxy

   • Adds external dependency and deployment complexity
   • Doesn't integrate with existing auth/observability stack
   • Less control over security and rate limiting

4. Full API Auto-Generation

   • Security risk: exposes entire API surface automatically
   • Proposed solution uses curated, explicit tool registration
   • Allows gradual, controlled expansion of MCP capabilities

Additional Context

Benefits

• AI-First Development: Enable AI coding assistants to interact with the application
• Ecosystem Integration: Join the growing MCP ecosystem (Claude Desktop, Cline, etc.)
• Controlled Expansion: Start with safe read-only tools, expand deliberately
• Production-Ready: Built-in auth, rate limiting, metrics, and observability
• Developer Experience: Local stdio mode for rapid development and testing

Technical Considerations

• FastMCP is built specifically for FastAPI integration
• Supports both SSE and HTTP POST transports
• Maintains compatibility with existing middleware and lifecycle management
• Minimal dependencies: reuses project infrastructure

Example Use Cases

1. AI coding assistant queries application health and metadata
2. External monitoring tools discover available endpoints via MCP
3. Controlled integration with documentation or search MCP servers
4. Future: AI agents perform complex multi-step operations via curated tools

References

• FastMCP Documentation
• Model Context Protocol Specification
• Anthropic MCP Announcement

Priority

Medium