Best AI Gateways for Centralized MCP Tool Routing

TL;DR: As AI agents move into production, MCP gateways have become essential infrastructure for centralized tool routing, security, and observability. Bifrost leads with unified LLM and MCP routing at microsecond-level latency. Kong AI Gateway, Lasso Security, MintMCP, and IBM ContextForge each serve distinct use cases ranging from security-first deployments to multi-cluster federation.

Why MCP Gateways Matter for Tool Routing

The Model Context Protocol (MCP), introduced by Anthropic in late 2024, has become the standard interface for connecting AI models to external tools, APIs, and data sources. But connecting agents directly to dozens of MCP servers creates what engineers call the N×M integration problem: every agent needs its own authentication, routing logic, and error handling for every tool it accesses. The result is brittle architecture that collapses under production load.

An MCP gateway solves this by acting as a centralized control plane. All agent-to-tool traffic flows through a single governed endpoint that handles routing, authentication, rate limiting, and observability. Gartner projects that by 2026, 75% of API gateway vendors will integrate MCP features as autonomous AI agents become embedded in enterprise applications.

Here are five AI gateways purpose-built for centralized MCP tool routing.

1. Bifrost

Platform Overview

Bifrost is an open-source, high-performance AI gateway written in Go that unifies LLM routing and MCP tool access through a single infrastructure layer. Built by Maxim (H3 Labs Inc.), Bifrost eliminates the need to deploy separate systems for model access and tool governance. It operates as both an MCP server and client simultaneously, enabling advanced routing, caching, and access control patterns that single-role gateways cannot achieve.

Organizations like Clinc, Thoughtful, and Atomicwork run Bifrost in production where both LLM routing and tool access are managed through one governed control plane.

Features

Bifrost's architecture delivers 11-microsecond latency overhead, making it one of the fastest gateways available for high-throughput AI workloads. Key capabilities include:

• Unified LLM and MCP gateway: A single OpenAI-compatible API routes requests across 12+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Mistral, Groq, Ollama, and others) while simultaneously managing MCP tool interactions.
• Native MCP integration: AI models access external tools including filesystem, web search, databases, and custom services through a standardized MCP interface. Agents discover available tools through Bifrost's gateway layer with centralized configuration controlling which tools are accessible to which teams.
• Code Mode: Reduces token usage by 50%+ for multi-tool orchestration. Instead of loading hundreds of tool schemas into context, AI models generate TypeScript orchestration code, dramatically cutting costs in complex agentic workflows.
• Semantic caching: Caches responses based on semantic similarity rather than exact match, reducing both latency and cost for repeated or near-duplicate queries.
• Enterprise governance: RBAC enforcement at the tool level, rate limiting to prevent runaway agent loops, automatic failover with weighted load balancing across providers, and built-in observability with native integration into Maxim AI's evaluation and observability platform.
• Open source under Apache 2.0: Full deployment flexibility with zero vendor lock-in.

What centralized MCP routing looks like in practice

The N×M integration problem disappears the moment all MCP traffic flows through one MCP gateway. Start Bifrost locally:

npx -y @maximhq/bifrost

Connect each MCP server in the dashboard at http://localhost:8080 once, then point every agent at the gateway instead of at each server individually. Every agent gets one endpoint, one auth flow, and one observability surface — Bifrost handles the routing, retries, and credential management at the gateway layer.

Two things are worth knowing on the first run:

• Tool definition overhead is the hidden cost. With centralized routing but no schema optimization, every tool from every connected server still lands in the model's context on every request. Five MCP servers with thirty tools each puts 150 definitions in front of the model before the prompt. Bifrost's Code Mode addresses this by exposing the tool surface as a virtual filesystem of Python stubs and letting the model fetch full definitions only when it decides to call them. The savings compound with tool count, not request count, which is why teams running 10+ servers see the largest reductions. Full benchmark methodology lives in the Bifrost MCP benchmark report.

• Governance is a gateway-layer concern, not an agent-layer one. Virtual keys, per-tool access control, and audit logs work the same way regardless of which agent or MCP server is involved. Configuring them once at the gateway means every new agent inherits the policy without code changes. This is the operational difference between "we connected MCP servers" and "we run MCP in production."

For teams comparing gateways at the procurement stage, the LLM gateway buyer's guide covers the criteria that matter at production scale. For teams already running tools through a gateway, the LLM cost calculator gives a quick view of the savings at current traffic.

Best For

Engineering teams that need MCP tool access unified with LLM routing, enterprise governance, ultra-low latency, and native observability in a single gateway.

2. Kong AI Gateway

Platform Overview

Kong AI Gateway extends Kong's established API management platform with AI-specific routing and transformation capabilities. Kong added MCP-aware features to its plugin ecosystem in 2025 and 2026, allowing teams already running Kong to extend their existing infrastructure to cover LLM and MCP traffic.

Features

Kong offers enterprise support contracts and SLAs, multi-cloud deployment options, and a mature plugin ecosystem for traffic management. MCP support is implemented through plugins rather than as a native first-class capability, which means complex MCP scenarios may require custom plugin development.

3. Lasso Security

Platform Overview

Lasso Security provides an open-source, security-first MCP gateway designed specifically for protecting agentic workflows. Launched in April 2025, it acts as a proxy and orchestrator that embeds security, governance, and monitoring capabilities into every MCP interaction. Lasso was named a 2024 Gartner Cool Vendor for AI Security.

Features

Lasso implements a triple-gate security pattern across three layers: AI (prompt filtering, PII detection), MCP (tool authorization, parameter validation), and API (rate limiting, authentication). Its plugin-based architecture supports real-time threat detection, MCP server reputation scoring that automatically blocks suspicious servers, and PII masking via Presidio integration. Structured JSON logging enables full audit trails.

4. MintMCP

Platform Overview

MintMCP is a managed MCP gateway focused on rapid deployment and compliance. It converts local STDIO-based MCP servers into production-ready services with minimal configuration, wrapping them with OAuth/SSO authentication and audit logging without requiring code changes.

Features

MintMCP holds SOC 2 Type II certification, which eliminates months of procurement friction for teams in regulated industries. One-click deployment handles the infrastructure complexity, and Virtual MCP servers expose only the minimum required tools per role, enforcing least-privilege access. The platform has a partnership with Cursor for validated production coding environments.

5. IBM ContextForge

Platform Overview

IBM ContextForge is a production-grade open-source AI gateway, registry, and proxy that federates tools, agents, models, and APIs into a single endpoint. It runs as a fully compliant MCP server and supports multi-cluster environments on Kubernetes.

Features

ContextForge provides multi-protocol support with REST-to-MCP translation, gRPC-to-MCP conversion, and agent gateway support for the A2A protocol. Its model gateway proxies LLM requests with OpenAI API spec compatibility across 8+ providers including watsonx, OpenAI, Anthropic, and Ollama. Multiple ContextForge instances automatically discover and share tool registries via mDNS without manual configuration.

How to Choose

The right MCP gateway depends on what your team is optimizing for. If you need a unified control plane for both LLM routing and tool governance with minimal latency overhead, Bifrost delivers the most complete feature set under an open-source license. Kong fits teams already invested in its API management ecosystem. Lasso is purpose-built for security-critical environments. MintMCP accelerates compliance-heavy deployments. ContextForge serves large-scale Kubernetes-native architectures.

For most engineering teams building production AI agents, the combination of native MCP support, Go-native performance, multi-provider routing, semantic caching, and enterprise governance makes Bifrost the strongest foundation for centralized MCP tool routing in 2026.

Ready to centralize your MCP tool routing? Get started with Bifrost or explore Maxim AI's evaluation and observability platform for end-to-end AI quality management.

FAQ

What is centralized MCP tool routing?

Centralized MCP routing means every agent in your stack talks to a single gateway endpoint instead of connecting to each MCP server directly. The gateway handles authentication, request routing, retries, observability, and access control across all connected servers. The architectural payoff is moving the N×M integration problem (every agent × every server) down to N+M (every agent connects to one gateway, every server connects to one gateway).

Why not connect agents directly to MCP servers?

Direct connections work for two or three servers. Beyond that, every new server multiplies the operational surface: every agent needs its own authentication for every server, error handling for every protocol variant, and observability for every endpoint. The brittleness shows up first as inconsistent retry logic and unattributable failures, then as governance gaps that block enterprise deployment.

How does an MCP gateway reduce token usage?

Two ways. The first is at the request layer; caching repeated tool calls so identical sub-queries don't re-execute. The second, more impactful pattern is dynamic schema loading. Instead of injecting every tool's full definition into every request, the gateway exposes tools as lightweight references and lets the model fetch full schemas only when it decides to call them. Token savings from dynamic loading compound with tool count, not request count.

Does an MCP gateway add latency?

A well-designed gateway adds single-digit milliseconds at the proxy layer. The bigger latency factor is what the gateway lets you avoid: redundant tool executions, cold-start auth flows, and round-trips to fetch tool definitions the model didn't need. For most production workloads, the gateway is net-faster than direct connections after caching and schema optimization kick in.

Can MCP gateways handle authentication for multiple identity providers?

The production-grade ones do. OAuth 2.1 became part of the MCP spec in mid-2025 and is now the expected baseline for enterprise deployments. The gateway sits between the agent and the identity provider, handling token issuance, refresh, and propagation to downstream MCP servers. Without this, every agent has to handle SSO directly, which doesn't scale past two or three integrations.

What governance features should an MCP gateway include?

At minimum: virtual keys (per-team or per-agent credentials that can be rotated independently), per-tool access control (allow-listing which agents can call which tools), audit logs (request-level traces with attribution), and rate limiting (per-key and per-tool budgets). Anything beyond this like drift detection, tool-call anomaly scoring, prompt injection screening; is a differentiator rather than a baseline.