Why We Suddenly Need Protocols
Agents today need access to filesystems, databases, HTTP APIs and SaaS systems. Every vendor has been inventing ad‑hoc “tool” formats and connectors. Without a standard, migrating a model or platform means ripping out and rewriting dozens of integrations.
That fragility is why a protocol layer matters: it decouples models from the specifics of how tools are invoked and how context is represented.
More practically: teams integrating models into complex stacks discovered that connectors leak assumptions — auth formats, payload shapes, and transport quirks — that create long‑lived lock‑in. A protocol provides a stable contract and a migration surface that tools can depend on.
What MCP Actually Does
Model Context Protocol (MCP) is a specification for exposing tools and data sources—files, HTTP APIs, databases—to any compatible model. It standardizes how context is described, how tools are discovered and invoked, and how results and telemetry are returned.
In practice MCP aims to make a project’s context portable: an editor could publish a workspace via MCP so different models see the same project state without bespoke adapters.
MCP defines primitives: a context description format, tool manifests, invocation envelopes and a telemetry contract. A model or runtime that implements MCP can discover available tools, negotiate auth, and invoke actions in a standardized way so connectors don’t depend on a single vendor’s runtime semantics.
That portability matters for procurement, compliance and long‑term maintenance: it lets teams experiment with model families (cloud and on‑prem) while keeping the same tooling surface for business logic.
The Other Camps: Proprietary Tool APIs and Custom Graph Runtimes
Not everyone is standardizing in the same way. OpenAI’s tools/functions, Anthropic’s tools API, LangChain runtime patterns, and index formats like LlamaIndex each offer different tradeoffs in transport, capabilities and vendor coupling.
Some systems prioritize tight integration and richer capabilities at the cost of portability; others opt for transport‑agnostic, minimal primitives that favor swapping runtimes later.
For example, OpenAI's tools/functions are convenient for immediate integration with their models, while LangChain accelerates developer productivity with higher‑level runtime patterns. Each choice embeds a set of assumptions about auth, data flows, and telemetry.
What’s at Stake: Portability, Security, Governance
Portability
Can you swap Anthropic ↔ OpenAI ↔ a local LLM without rebuilding connectors? The protocol choice determines how much work that swap requires.
Security & Governance
Protocols also shape where access policies live. Centralizing via a gateway lets the org enforce IAM and audit trails; exposing connectors everywhere makes policy enforcement harder and increases blast radius.
Choosing the wrong protocol can also complicate data residency and compliance. If tool invocation semantics require credential shipping into third‑party runtimes, you suddenly face hard limits for regulated workloads.
How Teams Should Decide in 2026
Evaluate criteria like vendor neutrality, ecosystem support, ability to run on‑prem, and alignment with your API gateway and IAM. Prefer open, portable approaches for long‑term flexibility, but insulate with your own gateway to retain control over security and telemetry.
Concrete checklist: (1) can you run the protocol on‑prem or behind your VPC? (2) does it map to your IAM and audit stack? (3) how large is the connector ecosystem? and (4) is the protocol evolved enough to handle your tool shapes (files, SQL, HTTP)? If you answer no to 1 or 2, you should be cautious.
In short: start with something MCP‑like, but wrap it behind a gateway and clear policies so you can evolve the protocol without reworking business logic.
Finally, invest in a thin translation layer in your gateway: this lets you support multiple protocols upstream while presenting a consistent internal API to your applications.
Real‑World Migration Example
One engineering org we advised built a gateway that accepted an MCP‑style manifest and translated it into a stable internal RPC. They first mapped auth flows to their IAM and added scoped tokens so downstream services never saw user credentials. In a staged rollout, they migrated three integrations—search, billing, and a document store—behind the gateway and verified parity with A/B tests.
The migration reduced connector churn: replacing a cloud model with a local LLM required no connector changes, only a runtime swap behind the gateway. The team measured lower incident rates and faster time‑to‑restore because telemetry surfaced mis‑mapped auth and payload shape mismatches during the rollout.
Beyond connectors, the gateway made it trivial to validate data residency and audit trails: SQL queries, object storage access (S3), and third‑party APIs were routed through the same policy layer. The org also added synthetic tests that exercised typical tool shapes—SQL reads, file fetches, and HTTP calls—so every connector shipped with a small regression harness. Those practices reduced surprise work during model upgrades and kept compliance teams satisfied.
Governance matters: require connector maintainers to publish a small spec (manifests, expected auth flows, and example payloads) and run migration drills quarterly. Those lightweight controls catch drift early and make audits far less painful. Run automated migration tests.
Implementation Checklist
- Shortlist protocols (MCP drafts, OpenAPI‑based tool manifests, LangChain runtimes) and evaluate on vendor neutrality and connector ecosystem.
- Prototype a gateway that accepts protocol X and exposes a stable internal API; add auth adapters for your IAM.
- Instrument telemetry: tool invocations, latencies, and auth failures; use it to validate connector assumptions.
- Build a thin translation layer to support multiple upstream protocols during migration.
- Run migration drills: swap a model runtime behind the gateway and verify call parity and audit trails.
For reference, see MCP community repos and discussions, OpenAI tools docs, and LangChain runtime docs as starting points.
Nexairi Take: Protocol Choice Is Your New Cloud Choice
Picking a protocol now is like picking a cloud provider a decade ago: invisible at first, defining later. Make the choice thoughtfully—favor openness, instrumentability, and a path to migrate if the ecosystem shifts.
Short term wins come from simple integrations and clear policies; long term wins come from building gateways and migration paths so your agents stay portable even as models change.
