Project Name
Ksolves Secures AI-Assistant Access to DOCSIS Networks With OAuth 2.1 MCP
A broadband network operator running large-scale DOCSIS cable infrastructure was scaling its use of enterprise AI assistants across network operations teams, but each new assistant needed its own hand-built connector to the same backend diagnostics services, and none shared a common security model. That fragility stood directly between AI copilots and the network health data they needed actually to act on.
Ksolves helped them close the gap with a single, standardized integration layer that any AI assistant can consume securely, with no backend code to touch. Every new AI assistant now connects through one standardized server instead of a custom-built integration, and OAuth 2.1 with Keycloak-backed authentication secures every tool call end to end.
- Tightly Coupled AI-to-Backend Integrations: Every AI assistant that needed network diagnostics data required its own custom connector wired directly to backend REST APIs, multiplying maintenance work with each new use case.
- No Standardized Interface for AI Consumption: Backend diagnostics services returned raw REST responses with no consistent contract, forcing each AI client to handle parsing and formatting differently.
- Difficult to Extend to New Diagnostic Domains: Adding a new network health capability meant rebuilding integration logic from scratch rather than plugging into a reusable pattern.
- No Production-Grade Authentication Layer: The integration point had no enterprise-grade authentication, blocking the server from moving into production at all.
- Risk of Identity Spoofing Across AI Clients: With no validated identity resolution, there was no reliable way to confirm which assistant or user was actually making a given request.
- No Phased Rollout Path for Security Controls: The team needed to introduce authentication without disrupting assistants already consuming the service in production.
Ksolves brought AI ML consulting expertise to two connected problems at once: giving AI assistants a standardized way to reach network diagnostics services, and locking that access down to enterprise security standards without breaking anything already live, governed by one principle: AI clients should never need to know how backend REST APIs are structured.
- Remote MCP Server Built From Scratch: A dedicated Model Context Protocol server, built on FastMCP and FastAPI, now gives any AI assistant a single, consistent entry point into network diagnostics services.
- Domain-Specific MCP Tool Set: Multiple purpose-built MCP tools each map to a specific network health capability, so new functionality gets added as a new tool instead of a new integration each time.
- Standardized REST-to-MCP Bridge: Existing backend REST APIs now connect to MCP tool interfaces with a standardized response format, removing the inconsistent parsing logic that used to live in every AI client separately.
- Extensible Architecture for Future Capabilities: New backend capabilities become additional MCP tools with minimal incremental engineering effort.
- OAuth 2.1 Authentication With Keycloak: OAuth 2.1 authentication backed by Keycloak establishes enterprise-grade access control ahead of production go-live.
- JWT Validation and Feature-Flagged Rollout: JWT validation and identity resolution run behind a feature flag, letting authentication roll out progressively without disrupting assistants already in production.
Technology Stack
| Category | Technology |
|---|---|
| Architecture | FastMCP |
| Integration | FastAPI |
| Integration | REST APIs |
| Security | OAuth 2.1 |
| Security | Keycloak |
| Security | JWT |
- Single Reusable Path Replaces Per-Assistant Connectors: Any AI assistant now connects through one standardized Remote MCP server, with no assistant-specific backend work required for every new use case.
- Future Integration Effort Cut by Extensible Tool Architecture: Adding a new backend capability now just means adding a single MCP tool to the existing server, replacing what used to require a new point-to-point integration built end to end.
- Production-Grade Authentication Closed a Critical Security Gap: OAuth 2.1 with Keycloak-backed JWT validation now secures every tool call end to end, clearing the blocker that had kept the server out of production.
- Zero-Disruption Security Adoption Through Feature-Flagged Rollout: Authentication rolled out behind a feature flag, letting the team introduce phased security adoption with no service interruption to assistants already consuming the service.
“Our AI assistants can now reach network diagnostics data through one secure, consistent interface, instead of a custom integration for every new use case.”
– Head of Network Engineering, Telecommunication
AI assistants at this broadband operator needed bespoke, tightly-coupled connectors to reach network diagnostics services, and no production-grade authentication existed to secure that access at all. Ksolves brought AI/ML expertise to build a single Remote MCP server on FastMCP and FastAPI, exposing standardized tools any AI assistant can consume, secured end-to-end with OAuth 2.1 and Keycloak-backed JWT validation.
Multiple domain-specific MCP tools now handle diagnostics interactions that used to require separate integration builds each time, and the feature-flagged, identity-resolved authentication model gives the organization a governance-ready security posture without disrupting anything already in production.
The extensible tool architecture positions the platform to onboard further AI assistants and additional diagnostics capabilities without repeating the original integration work from scratch.
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