As organizations adopt AI agents, the number of tools available to those agents grows quickly. One team may expose GitHub operations, another may build database integrations, and others may provide access to CRM systems, cloud infrastructure, monitoring platforms, and internal business applications.
Initially, connecting an AI application directly to a few MCP servers is manageable. The situation becomes more complicated when an organization has hundreds of internal tools maintained by different teams.
Every additional server introduces configuration, authentication, authorization, monitoring, and maintenance requirements. Agents also need to discover relevant tools without receiving thousands of tool definitions in every request.
An MCP gateway provides a centralized layer between AI clients and internal MCP servers. It handles discovery, routing, authentication, policy enforcement, and observability while allowing individual teams to maintain their own services.
This article explores how to design an MCP gateway for a large internal tool ecosystem, including its architecture, tool discovery strategy, security model, routing, and operational considerations.
Why Direct MCP Connections Become Difficult to Manage
Consider an organization with 200 internal MCP servers and approximately 1,000 tools.
These tools might include:
- Searching GitHub repositories and reviewing pull requests.
- Reading customer information from CRM systems.
- Querying application databases.
- Creating Jira issues and updating project status.
- Deploying applications to Kubernetes.
- Retrieving cloud infrastructure metrics.
- Managing employee and finance workflows.
If every AI client connects directly to every server, the organization must maintain a large number of individual connections and configurations.
This creates several problems.
Configuration sprawl
Each client needs to know which servers are available, where they are hosted, and how to authenticate with them. When a server changes its endpoint or credentials, multiple clients may need updates.
Excessive tool definitions
Sending every available tool definition to an AI model consumes context. Large descriptions and JSON input schemas can occupy a significant portion of the available context window before the actual user request is processed.
Inconsistent authorization
Different teams may implement authentication and permissions differently. One server might use OAuth, another might accept API keys, and another might have no meaningful access restrictions.
Difficult operational management
Monitoring hundreds of independent servers makes it harder to identify failing integrations, investigate unusual activity, and enforce common operational standards.
An MCP gateway addresses these problems by providing a shared management and execution layer.
Separate the Control Plane From the Data Plane
A scalable gateway should separate configuration management from request execution.
The control plane manages the catalog of MCP servers, tool definitions, ownership, permissions, and configuration versions.
The data plane handles incoming MCP requests and forwards approved operations to the appropriate backend.
A typical architecture looks like this:
AI Clients and Agents
|
v
MCP Gateway
|
+-----------+-----------+
| |
v v
Control Plane Data Plane
| |
MCP Registry Authentication
Tool Catalog Authorization
Ownership Tool Routing
Versioning Rate Limiting
Approvals Audit Logging
| |
+-----------+-----------+
|
v
Internal MCP Servers
|
+--------------+--------------+
| | |
v v v
GitHub Database Jira
Service Service Service
| | |
+--------------+--------------+
|
v
Internal APIs and Systems
The control plane should not be responsible for processing every tool invocation. Similarly, the data plane should not become the only place where server ownership and configuration are maintained.
This separation allows teams to update tool definitions without requiring every gateway instance to be redeployed.
Build a Central MCP Registry
The registry is the source of truth for available MCP servers and their tools.
Instead of maintaining server configurations independently in every AI application, service owners register their integrations in the central catalog.
A server registration should contain information such as:
- Unique server identifier.
- Display name and description.
- Backend endpoint or connection configuration.
- Owning team.
- Authentication requirements.
- Available tools.
- Tool input schemas.
- Data classification.
- Environment and deployment status.
- Configuration version.
A simplified registry record might look like this:
{
"serverId": "github-service",
"name": "GitHub Integration",
"description": "Access approved GitHub repository operations",
"owner": "developer-platform",
"environment": "production",
"transport": "streamable-http",
"endpoint": "https://internal.example.com/github/mcp",
"status": "approved",
"tools": [
"search_repositories",
"get_pull_request",
"list_commits"
]
}
The endpoint is illustrative. A real registry should also reference approved authentication configuration rather than storing reusable credentials directly in a catalog record.
A useful ownership model requires every registered server to have an accountable team. This makes it easier to investigate failures, approve schema changes, and retire unused integrations.
The registry should also maintain version history. If a service owner changes a tool's description, schema, or permissions, the change should be reviewed before it becomes available to agents.
Discover Tools Without Loading Everything
Tool discovery becomes one of the most important design decisions when the registry contains hundreds or thousands of tools.
A naive gateway could expose every tool through one large tools/list response. Although this may appear convenient, it creates unnecessary context consumption and makes tool selection more difficult.
A better approach is progressive discovery.
The gateway initially exposes a small set of discovery operations. An agent uses them to identify relevant servers and retrieve only the schemas it needs.
For example, the gateway might expose three discovery tools:
| Discovery tool | Responsibility |
|---|
| discover_server | Find relevant MCP servers using a description or task intent |
| discover_tools | Find tools within a selected server |
| get_tool_schema | Retrieve the complete input schema for a specific tool |
The workflow looks like this:
- The user asks the agent to investigate a failed deployment.
- The agent searches the registry for deployment and monitoring services.
- The gateway returns relevant server information.
- The agent requests tools from the selected servers.
- The gateway provides the required tool schemas.
- The agent invokes the selected operation through the gateway.
This prevents every tool definition from being loaded into the model context at the beginning of a conversation.
Combine keyword and semantic search
A registry can support both keyword-based and semantic discovery.
Keyword search works well when the user or agent already knows the relevant terminology.
For example:
Search tools for: get_pull_request
Semantic search is useful when the request describes an outcome rather than an exact tool name.
For example:
Find a tool that can identify why a production deployment failed.
The gateway can retrieve relevant tool descriptions using a combination of text matching, metadata filtering, and vector similarity.
The final results should include the tool name, server identity, description, required permissions, and a reference to its schema.
Search relevance should not determine authorization. Finding a tool means it may be useful; it does not mean the caller is allowed to execute it.
Route Tool Calls Through a Unified Endpoint
Once an agent selects a tool, the gateway must identify the correct backend and forward the request.
For example, an agent might invoke a tool named get_pull_request.
The gateway needs to resolve the tool to its registered server, verify access, validate the arguments, and forward the call.
A simplified routing configuration could look like this:
interface ToolRoute {
serverId: string;
toolName: string;
endpoint: string;
requiredScopes: string[];
}
const routes: ToolRoute[] = [
{
serverId: "github-service",
toolName: "get_pull_request",
endpoint: "https://github.internal/mcp",
requiredScopes: ["github:read"]
},
{
serverId: "jira-service",
toolName: "create_issue",
endpoint: "https://jira.internal/mcp",
requiredScopes: ["jira:write"]
}
];
This example illustrates the routing metadata. A production implementation should use a validated registry and a maintained MCP client or proxy implementation rather than manually forwarding arbitrary JSON-RPC requests.
A gateway should preserve the relevant MCP protocol behavior, including initialization, capability negotiation, tool discovery, error handling, and notifications supported by its chosen protocol version.
It should also support different backend technologies. Some internal services may already expose native MCP endpoints, while others may use HTTP or gRPC APIs.
The gateway can adapt approved APIs into MCP tools, allowing AI clients to use a consistent interface without requiring every backend service to implement MCP independently.
Implement Authentication and Tool-Level Authorization
Centralized routing does not automatically provide secure access.
The gateway must establish who is making a request and determine which operations that identity can perform.
Authentication answers the question:
Who is calling the gateway?
Authorization answers:
Which server, tool, and operation can that caller access?
For enterprise environments, the gateway should integrate with the organization's identity provider and supported OAuth-based authorization flows.
It should also distinguish between interactive users and automated workloads. A user acting through an AI assistant may need delegated permissions, while a scheduled agent may operate under a service identity.
Authorization should be evaluated for each tool invocation, not only when the agent first discovers the server.
For example:
| Identity | Tool | Access |
|---|
| Developer | search_repositories | Allow |
| Developer | create_pull_request | Allow |
| Developer | delete_repository | Deny |
| Release agent | get_deployment_status | Allow |
| Release agent | deploy_to_production | Approval required |
The gateway should also support:
- Server-level permissions.
- Tool-level permissions.
- Environment-specific access.
- Tenant isolation.
- Scope validation.
- Human approval for sensitive operations.
A tool description or annotation must never be treated as proof that an operation is safe. The gateway should apply trusted policies independently of metadata supplied by a backend server.
Validate Tool Arguments Before Execution
A tool call may contain valid JSON and still represent an invalid or dangerous operation.
For example, a database tool might accept a query parameter. The gateway should validate the input against the registered JSON Schema before forwarding the request.
Validation should cover:
- Required properties.
- Data types.
- Allowed values.
- String and collection size limits.
- Unexpected fields where strict validation is appropriate.
- Resource identifiers and tenant boundaries.
However, schema validation alone is not sufficient.
A valid request to delete a production database can still be unauthorized. Business-level validation and access policies must be enforced by the gateway and the backend service.
For destructive operations, introduce an approval workflow. The approval should be bound to the actual operation and its parameters, so that an agent cannot obtain approval for one action and then modify the request before execution.
Manage Configuration Changes Without Restarting the Gateway
A large tool ecosystem changes frequently. New servers are registered, tools are added, and permissions are updated.
Requiring a full gateway deployment for every registry change creates unnecessary operational overhead.
Instead, the data plane can consume versioned configuration updates from the control plane.
A typical process is:
- A service owner submits a server or tool change.
- The registry validates the metadata and schema.
- The required owner or security approval is obtained.
- A new configuration version is published.
- Gateway instances receive the update.
- The new configuration becomes active.
- The previous version remains available for rollback.
Use immutable configuration versions and track which version each gateway instance has loaded.
This makes it possible to investigate why two gateway instances behaved differently during a configuration rollout.
For critical permission changes, define how quickly the data plane must receive revocations. Do not rely exclusively on long-lived cached authorization decisions.
Add Observability to Every Tool Invocation
When hundreds of internal tools are available, logs from individual MCP servers are not enough to provide a complete operational view.
The gateway should record the complete invocation lifecycle.
Useful telemetry includes:
- Authenticated caller identity.
- Client or agent identity.
- Backend server identifier.
- Tool name.
- Request correlation ID.
- Authorization decision.
- Backend response status.
- Execution duration.
- Retry count.
- Error category.
- Configuration version.
Avoid logging access tokens, passwords, complete sensitive tool arguments, or confidential response content.
Metrics should help answer operational questions such as:
- Which tools receive the most requests?
- Which backend servers have the highest error rates?
- Which tools have unusually high latency?
- How often are authorization requests denied?
- Which agents generate excessive requests?
- How many tool calls fail because of schema validation?
Distributed tracing is also useful. A trace can connect the agent request, gateway processing, backend MCP server, and downstream API call.
This provides a practical way to investigate failures that cross multiple internal services.
Design for Scale and Failure
A gateway becomes a critical dependency when many AI applications rely on it. Its availability and resource management therefore need deliberate attention.
Several design decisions are important.
Horizontal scaling
Keep request-routing instances stateless where the selected MCP transport and session model permit it. This allows gateway replicas to scale independently.
If the deployment uses stateful sessions, design explicit session affinity or shared session management rather than assuming every request can reach any replica.
Connection management
Reuse backend connections where appropriate, but avoid allowing one unhealthy server to consume all available connections.
Set connection limits and timeouts per backend.
Rate limiting
Apply limits at several levels:
- Individual user.
- Agent or workload identity.
- MCP server.
- Tool.
- Tenant.
This protects internal systems from runaway agents and accidental request loops.
Circuit breakers
If a backend repeatedly fails, temporarily stop sending it requests. Return a controlled error and allow recovery checks before restoring normal traffic.
Caching
Cache server metadata, tool descriptions, and schemas when appropriate. Use configuration versions and expiration policies to avoid serving outdated definitions.
Do not cache sensitive tool results indiscriminately. Result caching requires authorization-aware keys and clear data-retention rules.
Failure isolation
A problem in the Jira integration should not prevent an agent from accessing an otherwise healthy GitHub integration.
Separate backend connection pools, request queues, and operational health checks where possible.
Common MCP Gateway Design Mistakes
| Mistake | Why it becomes a problem | Better approach |
|---|
| Exposing every tool to every agent | Consumes context and makes discovery harder | Use progressive discovery |
| Treating discovery as authorization | A visible tool may still be restricted | Authorize every invocation |
| Storing credentials in registry records | Increases the impact of a registry compromise | Use a dedicated secrets system |
| Routing by tool name alone | Different servers can expose tools with identical names | Use server-qualified tool identifiers |
| Logging complete tool payloads | May expose sensitive business data | Apply structured redaction |
| Updating schemas without approval | Can change tool behavior unexpectedly | Use versioned configuration and owner approval |
| Sharing one connection pool across all backends | One unhealthy service can affect unrelated tools | Isolate backend resources |
| Relying only on gateway authorization | Backend services may still need their own access checks | Enforce defense in depth |
Conclusion
An MCP gateway provides a common infrastructure layer for organizations operating large internal tool ecosystems. It reduces direct connections between AI clients and backend systems while creating a central place for discovery, routing, authorization, and observability.
The most important architectural decision is to separate the control plane from the data plane. The registry manages server ownership and tool configuration, while the gateway handles runtime execution.
For hundreds of internal tools, progressive discovery is equally important. Agents should retrieve only the tools relevant to their current task instead of loading the entire catalog into their context.
A production-ready gateway also needs invocation-level authorization, schema validation, configuration versioning, audit logging, rate limiting, and failure isolation. These controls allow teams to expand internal AI integrations without turning tool access into an unmanaged collection of independent connections.
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