Multi-Agent Protocol & Tool Bus

Universal Model Context Protocol (MCP) Multi-Agent Gateway

Anthropic MCP open-standard gateway facilitating cross-agent capability discovery, cryptographic capability authorization, and real-time SSE tool execution across 50+ enterprise SaaS connectors.

📖 Project Overview

As organizations scale autonomous agents across different teams and frameworks (LangChain, AutoGen, CrewAI, Custom Python), agents become trapped in proprietary silos. They lack a uniform protocol for exposing capabilities, introspecting dynamic schemas, and securely sharing operational tools.

This platform engineers a centralized Model Context Protocol (MCP) Multi-Agent Gateway compliant with Anthropic's open specification. It establishes an asynchronous Server-Sent Events (SSE) multiplexing transport layer that exposes a unified registry of enterprise resources, prompts, and tools.

Heterogeneous agents negotiate permissions cryptographically, inspect input schemas on the fly, and execute actions with strict per-tenant rate-limiting and audit tracing. The bridge currently supports 50+ production integrations including AWS IAM, Kubernetes APIs, GitHub Enterprise, Jira, Datadog, and Snowflake.

⚙️ Deployment Checklist & Runbook

Initialize FastMCP Tool Server

Define strongly-typed Pydantic tools exposed over the MCP protocol:

python

from mcp.server.fastmcp import FastMCP
from pydantic import BaseModel, Field

mcp = FastMCP("enterprise-infra-gateway")

class ClusterQuery(BaseModel):
    namespace: str = Field(description="Target Kubernetes namespace")
    resource_type: str = Field(default="pods", description="Resource kind")

@mcp.tool()
async def query_cluster_health(query: ClusterQuery) -> str:
    """Introspect real-time Kubernetes cluster health via MCP."""
    return f"Status for {query.resource_type} in {query.namespace}: All 14 pods Healthy"

if __name__ == "__main__":
    mcp.run(transport="sse")

Configure SSE Multiplexing Transport

Establish persistent Server-Sent Events channel for bidirectional agent streaming:

python

from mcp.client.sse import sse_client
from mcp.client.session import ClientSession

async with sse_client("http://mcp-gateway:8080/sse") as streams:
    async with ClientSession(*streams) as session:
        await session.initialize()
        tools = await session.list_tools()
        result = await session.call_tool("query_cluster_health", {"namespace": "production"})

Deploy Zero-Trust RBAC Policy Sidecar

Enforce Open Policy Agent (OPA) validation on all incoming A2A tool dispatches:

rego

package mcp.authz

default allow = false

allow {
    input.agent.role == "sre-lead"
    input.tool.name == "query_cluster_health"
}

allow {
    input.agent.role == "admin"
    input.tool.scope == "read-write"
    input.client_cert_verified == true
}

🔄 Configuration & Environment Keys

Environment Variable	Description	Suggested Value
`MCP_TRANSPORT_MODE`	Transport communication layer.	`sse` (or `stdio`)
`MCP_SERVER_PORT`	Port for SSE events stream and POST routing.	`8080`
`MAX_CONCURRENT_SSE_CLIENTS`	Multiplexing connection pool cap.	`2500`
`TOOL_EXECUTION_TIMEOUT_SEC`	Maximum duration before tool abort.	`30`

📊 Architectural Workflow

graph TD
    AgentA([LangGraph Orchestrator]) -->|SSE Connect / Handshake| MCPGateway[Universal MCP Gateway]
    AgentB([Claude 3.7 Computer-Use]) -->|SSE Connect / Handshake| MCPGateway
    MCPGateway -->|Validate Token| OPASidecar[OPA RBAC Engine]
    OPASidecar -->|Granted| SchemaRegistry[Dynamic Tool & Schema Registry]
    SchemaRegistry -->|List Capabilities| MCPGateway
    MCPGateway -->|Execute Tool Call| ExecutionWorker[Sandboxed Tool Worker]
    ExecutionWorker -->|Secure Connector| AWS[(AWS Cloud APIs)]
    ExecutionWorker -->|K8s SDK| K8s[(Kubernetes Cluster)]
    ExecutionWorker -->|Database Query| DB[(PostgreSQL Data Lake)]
    ExecutionWorker -->|Return Result| MCPGateway
    MCPGateway -->|JSON-RPC Response| AgentA

🛠️ Useful CLI Reference

# Introspect registered MCP server tools over SSE:
curl -N http://localhost:8080/sse

# Inspect live active MCP connections:
curl http://localhost:8080/api/mcp/health

# Run automated MCP compatibility regression suite:
npm run test -- tests/mcp-universal-gateway.test.js