Building a Unified AI Gateway: How IBM's ContextForge Federates MCP, REST, and Agent Protocols

Hook

Your LLM agent can't call your REST APIs without custom glue code, your MCP servers lack centralized auth, and you have no visibility into token costs across 47 different tools. Welcome to the fragmented AI tooling landscape of 2024.

Context

The Model Context Protocol (MCP) promised to standardize how AI agents discover and invoke tools, but enterprises don't live in greenfield worlds. You've got decades of REST and gRPC services, emerging A2A (Agent-to-Agent) protocols, and a patchwork of authentication schemes. Each AI framework—LangChain, Claude Desktop, custom agent frameworks—implements tool calling differently. The result? Every integration becomes bespoke plumbing, observability is an afterthought, and enforcing consistent rate limits or guardrails requires touching dozens of codebases.

ContextForge addresses this by functioning as an API gateway specifically designed for AI workloads. Like Kong or Tyk for traditional microservices, it sits between your AI clients and backend services, but with deep awareness of MCP semantics, tool schemas, and LLM-specific concerns like token counting and cost tracking. It translates protocols bidirectionally—your MCP client can invoke a legacy REST endpoint as if it were a native MCP tool, and vice versa. The federation layer adds centralized discovery: register tools once, expose them across all connected AI systems with unified authentication and observability.

Technical Insight

ContextForge's architecture centers on a FastAPI application using asyncio for concurrent request handling across multiple transport protocols. The core abstraction is the protocol adapter pattern—each backend type (MCP, REST, gRPC, A2A) implements a common interface that the gateway orchestrates. When an MCP client calls tools/list, ContextForge queries its registry (backed by Redis in distributed mode), aggregates tools from all registered backends, and returns a unified response adhering to the MCP schema.

Here's what a basic tool registration looks like in the ContextForge configuration:

# config.yaml snippet
backends:
  - name: "legacy-weather-api"
    type: "rest"
    base_url: "https://api.weather.internal/v2"
    endpoints:
      - path: "/current"
        method: "GET"
        mcp_tool_name: "get_weather"
        schema:
          input:
            location: {type: "string", description: "City name"}
          output:
            type: "object"
            properties:
              temperature: {type: "number"}
              conditions: {type: "string"}
    auth:
      type: "jwt"
      issuer: "internal-auth-service"

When an AI agent invokes get_weather, ContextForge intercepts the MCP tools/call request, validates JWT credentials, checks rate limits, translates the MCP tool invocation into an HTTP GET with query parameters, executes the call, and wraps the REST response back into MCP format. The translation happens in the protocol adapters:

# Simplified adapter logic
class RESTAdapter:
    async def call_tool(self, tool_name: str, arguments: dict) -> dict:
        endpoint = self.registry.get_endpoint(tool_name)
        
        # MCP arguments → REST parameters
        if endpoint.method == "GET":
            response = await self.http_client.get(
                f"{endpoint.base_url}{endpoint.path}",
                params=self._map_arguments(arguments, endpoint.schema)
            )
        
        # REST response → MCP result format
        return {
            "content": [{"type": "text", "text": response.json()}],
            "isError": response.status_code >= 400
        }

The federation capability scales this horizontally. Multiple ContextForge instances share a Redis-backed registry, allowing tool definitions registered in one datacenter to propagate to all gateways. Each instance maintains local caches with TTLs, reducing Redis round-trips while ensuring eventual consistency. The Kubernetes deployment pattern uses a StatefulSet for the registry service and a Deployment for gateway replicas behind a LoadBalancer.

Observability is first-class through OpenTelemetry integration. Every tool invocation generates spans with LLM-specific attributes—model name, token counts, estimated costs—that flow to your tracing backend (Jaeger, Phoenix, Zipkin). The plugin architecture extends this: custom plugins can inject middleware for logging, transform requests, or implement domain-specific guardrails:

# Example guardrail plugin
class PIIFilterPlugin(ContextForgePlugin):
    async def on_tool_call(self, context: CallContext) -> CallContext:
        # Scan input arguments for SSN patterns
        for key, value in context.arguments.items():
            if isinstance(value, str) and self._contains_ssn(value):
                context.reject("PII detected in tool arguments")
        return context

The 7,000+ test suite validates protocol compliance against the MCP specification and ensures backward compatibility as the protocol evolves. ContextForge tracks specific MCP versions and can negotiate protocol versions with clients, handling the messy reality of clients and servers at different specification levels.

Gotcha

The operational complexity is real. Running ContextForge in production means operating a FastAPI service, a Redis cluster (or accepting single-node risk), and monitoring the interaction between potentially dozens of backend services. When something breaks—say, a downstream REST API times out—debugging requires understanding both the MCP protocol flow and your backend's behavior. The error messages ContextForge returns to MCP clients sometimes lose fidelity during protocol translation; a rich gRPC error with multiple details might collapse into a generic MCP error structure.

The MCP protocol itself is still maturing (the specification is under active development), and ContextForge necessarily tracks specific versions. If you're running bleeding-edge MCP servers or clients experimenting with proposed protocol extensions, you may hit incompatibilities. The documentation acknowledges this by referencing GitHub issues for setup guides, suggesting the docs haven't fully caught up to the feature set. You'll need to be comfortable reading source code and occasionally diving into issue threads. Federation adds latency—every tool discovery request now involves registry lookups, cache checks, and potential cross-datacenter RPCs if you're geo-distributed. For latency-sensitive applications where every millisecond matters, the gateway layer is overhead you need to justify with the governance and observability benefits.

Verdict

Use ContextForge if you're building AI systems in an enterprise environment with heterogeneous tool landscapes—legacy REST APIs, emerging MCP servers, multiple authentication domains—and you need centralized governance, rate limiting, and observability without refactoring every backend. It's particularly valuable when standardizing on MCP as your long-term protocol but needing to integrate existing services during migration. The federation capabilities shine in multi-team, multi-cluster deployments where tool discovery and consistent policy enforcement matter more than minimal latency.

Skip ContextForge if you have a small, homogeneous set of tools already speaking MCP natively, or if operational simplicity is paramount and you can't justify running Redis and managing a gateway layer. For solo developers or small teams with direct control over all backend services, the protocol translation and federation features are overkill—you're better off with direct MCP implementations or lightweight frameworks like LangChain. Also skip it if you're experimenting with rapidly changing AI architectures; the gateway layer adds coupling and upgrade coordination overhead that slows down iteration.