mcpgate vs IBM ContextForge

The case for IBM ContextForge

Multi-protocol federation. ContextForge isn’t just an MCP gateway — its README explicitly states it federates “MCP, A2A, and REST/gRPC APIs.” If you want one endpoint that fronts everything from MCP-spec tools to legacy gRPC services to A2A agents, that’s a much wider mandate than mcpgate currently has.

Multi-cluster scale. The README is explicit about multi-cluster federation as a core feature. If you’re running gateways in 3 regions and need them to auto-discover each other, mcpgate doesn’t have that — it’s a single-instance gateway today.

Deployment target coverage. ContextForge documents deployment to Docker, Helm/Kubernetes, AWS ECS/EKS, Azure AKS, Google Cloud Run, IBM Code Engine, and OpenShift. mcpgate is Docker Compose first; everything else you build yourself.

Brand + market validation. ~3,719 stars and ~661 forks (verified 2026-05-17 via the GitHub API), plus IBM’s name, plus an active commit cadence — together that’s a credibility floor mcpgate doesn’t match today. mcpgate is early stage: public since March 2026 For procurement-heavy environments where “has IBM backing” or “has talked-to references at our scale” is a checkbox, ContextForge has the day-one signal. That’s an early-stage gap, not a product gap — but it’s a real consideration for evaluators who pattern-match on adoption signals.

License clarity. Apache 2.0, OSI-approved. If your org has a hard policy against source-available licenses, BSL 1.1 is a veto on mcpgate.

The case for mcpgate

Depth on the common cases, plus an import path for everything else. ContextForge federates whatever you point it at — its strength is the composition surface. mcpgate’s default catalogue is smaller (22 hand-written first-party integrations) but each one carries deep per-service knowledge: Markdown-to-Jira-ADF conversion, Confluence storage-format peculiarities, Slack channel name resolution — baked in, not user-config. For anything we don’t ship pre-built, mcpgate has the same composition story ContextForge does: OpenAPI import for any REST API, any MCP server reachable via URL. The reachable surface ends up similar; mcpgate biases toward depth-for-the-common-22 and lets you import the rest, ContextForge biases toward federation-as-default.

PII handling is the differentiator nobody else has. Neither ContextForge nor Obot nor Docker MCP Gateway mentions PII pseudonymization in their README or docs. mcpgate’s pseudonym layer is the sharpest functional difference: sensitive fields (emails, names, phone numbers, customer IDs) are pseudonymized outbound, the mapping stays on-prem encrypted with a 24h TTL, and real values are rehydrated when the agent calls back into the source system. The practical consequence: your LLM provider literally never sees real names. For security reviews that ask “how do you prevent customer names from leaking?”, ContextForge’s answer is “via your access policies” — mcpgate’s answer is “structurally, the LLM never sees them.”

End-user agency via per-user hooks. ContextForge offers operator-level governance: centralized policy, RBAC, observability. mcpgate adds a second layer — each end-user can write their own policy hooks (YAML, hot-reloaded) from their own AI client. Practical example: a PM defines “always assign Jira tickets to me, default project = PRODUCT” without filing a request with the operator. ContextForge’s governance model is one-way (operator down). mcpgate is two-way.

Lower setup overhead for the single-team case. ContextForge is built to scale to multi-cluster federation; that scaling story comes with operational surface area (Redis, observability backends, federation config). For a team of 5-200 running one gateway, that’s overhead you pay without using. mcpgate is docker compose up and through the setup wizard in ~10 minutes.