The MCP use cases worth tracking

MCP went from emerging standard to genuine production foundation through 2025. By year-end, the major vendors all support it, the open-source ecosystem is real, and a meaningful slice of new agent platform work is built on it. The broad-narrative version of "MCP is the standard for AI tool integration" is true but undersells what's actually happening, the specific use cases that have shipped on MCP are more interesting than the generic framing.

Worth being specific about which use cases to track because the ones that matter aren't always the ones the marketing layer points at.

The use cases that actually shipped

Six categories where MCP-shaped solutions have moved from prototype to production through 2025:

Cross-vendor tool integration. The original use case. An agent built against Claude can use the same MCP-server-wrapped tools as an agent built against GPT-5 or Gemini. The portability is real; the friction is small; this is the lowest-friction path to using MCP in any deployment.

Memory-as-a-service. Memory hygiene gets cleaner when memory is an MCP server. Read, write, and forget operations as standard MCP tools. The agent's memory layer becomes pluggable; the privacy story improves; vendor lock-in on memory shrinks.

Policy decision endpoints. The OPA-and-AI pattern wraps OPA as an MCP server. The agent calls "is this allowed" via MCP rather than embedding policy logic. Policy enforcement becomes uniform across the agent population.

Observability and audit hooks. Every agent action flows through MCP-shaped observability servers. The audit trail is uniform across actions; the queries against it work the same regardless of what the underlying action was. This is what makes tool-call logging tractable at scale.

Knowledge-base / RAG integration. Wrapping a vector store or document store as an MCP server lets multiple agents query the same knowledge base with consistent semantics. The retrieval layer becomes a service rather than per-agent code.

Cross-platform tool sharing in personal AI. Personal AI assistants increasingly use MCP servers for everything from calendar integration to email handling to file system access. The same MCP server provides the same capability to whichever agent surface the user is currently in.

These are real use cases. Each one has working production deployments by year-end 2025. The cumulative effect is that MCP became load-bearing for non-trivial AI infrastructure faster than I'd predicted.

The use cases worth tracking going forward

A few emerging patterns that are early but consequential:

MCP-as-deployment-unit. Tools shipping as MCP servers with associated policies, evaluation suites, and observability hooks all in one bundle. The deployment unit is "tool plus everything that makes the tool operable" rather than "tool plus a separate set of operational concerns." This pattern is starting to mature in 2025; expect more in 2026.

Multi-agent coordination via MCP. Agents communicating with each other as MCP servers. The pattern lets agents from different platforms collaborate without each platform vendor having to ship a multi-agent SDK. Early; the standardization isn't there yet; the use cases are interesting.

MCP for personal-context portability. The agent that knows you in Claude could, in principle, expose its memory and context via MCP servers that another agent in another platform could read from. The data-portability question I keep raising could be partially addressed at this layer. Not there yet; the pattern is gettable.

MCP-based credential brokering. A separate MCP server holds the actual credentials; agents request actions and the broker executes with the credentials, returning structured results. The capability-isolation pattern made architectural rather than DIY.

MCP for AI model selection. A routing MCP server that picks the right model for a given query based on configured rules. Lets the agent code stay model-agnostic; lets the routing decisions live in one configurable place.

These are the patterns to watch. Each one has working examples in late 2025; each one will mature meaningfully in 2026.

Where MCP doesn't fit

Worth being honest about the use cases where MCP isn't the right answer:

High-throughput streaming workloads. The MCP request-response shape doesn't fit cleanly to streaming responses. Pure-MCP architectures struggle with real-time streaming patterns; they're better-served by other protocols.

Very-low-latency inline workloads. When the latency budget is sub-10ms, the MCP overhead matters. For these cases, direct integration is faster.

Workloads that benefit from rich type systems. MCP's structured-input format is JSON-shaped; for cases where the team would benefit from richer typing (algebraic data types, generics), the MCP wrapping is awkward.

Single-vendor deployments where portability isn't valued. When the team is fully committed to one vendor's stack and has no intention to switch, the MCP overhead may not pay back. Direct integration is simpler in this case.

These aren't reasons not to use MCP broadly. They're reasons to be deliberate about per-use-case decisions.

What changed about MCP through 2025

A few specific shifts that took the standard from "interesting" to "load-bearing":

The broader vendor support landed. Every major model vendor shipped MCP support; the open-source ecosystem followed. The "is MCP a Anthropic thing" question that lingered into early 2025 is settled.

The routing layer matured. MetaMCP and the routing-layer category made MCP usable at production scale. Before this, the per-server wiring was the operational problem; after, the routing layer abstracts it.

The security model got better-specified. Authentication, authorization, capability scoping, the patterns are converging. Early MCP servers were too permissive; the late-2025 maturity model includes proper scoping by default.

The performance optimization layer formed. Caching at the routing layer, connection pooling, batching where possible. The performance characteristics that made early MCP feel slow are mostly addressed.

The tooling around MCP server development matured. Templates, generators, testing frameworks. Building a new MCP server in late 2025 is a fast project; in early 2025 it was a slog.

These collectively are why MCP went from "interesting standard" to "load-bearing infrastructure." Each individual change is small; the cumulative effect is qualitative.

What I'd watch in 2026

Three specific MCP-related developments to track:

Cross-platform agent collaboration via MCP. The pattern is starting; if it matures, it becomes the foundation for multi-vendor agent ecosystems. Watch for standardization efforts in this space.

Personal-context portability via MCP-memory. The use case I keep raising. Watch for both open-source projects and vendor-led standards work here.

Performance-optimized MCP runtimes. As the MCP surface grows, the performance optimization becomes more important. Watch for runtime improvements that make MCP usable for higher-throughput workloads than it currently is.

MCP in regulated environments. SOC 2, ISO 27001, HIPAA-relevant deployments using MCP. Watch for the audit-framework guidance that emerges as MCP becomes common in these environments.

The honest summary

MCP in late 2025 is the foundation I called for in the spring momentum piece, with a year of maturation behind it. The use cases that matter aren't always the broad-platform-pitch ones; they're specific patterns that have proven out in production.

The teams building serious AI infrastructure in 2026 should be building on MCP unless they have specific reasons not to. The lock-in protection alone justifies the slight overhead in most cases. The richer tooling effects compound from there.

Worth being specific about which use cases to invest in rather than treating MCP as a generic solution. The patterns above are the ones with the highest leverage in late 2025; the ones to watch for 2026 are where the next round of value creation happens.