MCP (Model Coordination Protocol)

The Model Coordination Protocol (MCP) is a foundational component of our infrastructure, enabling structured communication, fallback handling, and routing logic between AI agents, models, and services.

It acts as the control plane that determines which models run, in what order, under what conditions, and how their results are processed and validated.

What is MCP?

At its core, MCP is a lightweight coordination layer that abstracts away backend complexity while giving developers fine-grained control over execution paths. It is implemented as a stateless protocol with optional runtime hooks and middleware extensions.

Key responsibilities:

• Model selection based on input metadata and execution context
• Fallback orchestration across prioritized candidates
• Pre- and post-processing hooks (e.g., schema validation, prompt rewriting)
• Cross-agent communication with structured message passing

Protocol Anatomy

Each MCP execution cycle involves the following stages:

1. Intent Dispatch
   MCP receives a structured intent from an agent or application—this includes the desired action, input payload, and environment context.

2. Model Matching
   A set of candidate models are scored and selected using dynamic heuristics, e.g., confidence thresholds, availability, and latency cost.

3. Execution Routing
   The best-fit model is executed via its Endpoint. If it fails or violates policy, fallback models are triggered.

4. Post-Processing
   MCP applies format normalization, type validation (via Pydantic AI), and optionally re-queues the request for retry if conditions are met.

Integration Points

MCP integrates tightly with our:

• Agent2Agent (A2A) Protocol – to coordinate agent-level task handoffs
• TensorOne GraphQL API – to invoke and monitor serverless endpoints
• Prompt Rewrite Engine – to sanitize, enrich, or alter input prompts dynamically
• Model Metrics Collector – to log success/failure, response time, and fallback usage

Benefits of Using MCP

• Reliability: Every model execution includes structured fallback logic
• Modularity: Easily plug in or replace models without changing orchestration logic
• Security: Pre/post execution hooks allow for filtering, sanitization, and auditing
• Observability: Runtime events are logged and can be analyzed for performance tuning

Example Use Case

A user query is received by an agent. That agent issues an MCP intent:

{
  "intent": "qa.classify",
  "input": "What are the main risks of using synthetic datasets?",
  "priority": ["gpt-4", "claude-3", "internal-benchmark"],
  "max_retries": 2
}

MCP:

1. Routes the query to gpt-4 endpoint
2. Validates the schema of the result
3. On error, re-routes to claude-3 with an adjusted prompt
4. Returns a response payload with execution metadata to the originating agent

Future Additions

We’re currently extending MCP with:

• Context-aware model switching based on long-term dialogue states
• Semantic caching layer to prevent redundant calls
• Policy engine for zero-trust endpoint execution
• Load balancing between redundant endpoints

MCP is the coordination layer that makes multi-model, multi-agent systems actually usable. Without it, there would be no safety nets, no fallback logic, and no way to dynamically adapt execution paths in real time.

It’s not just a router—it’s a strategic mind behind intelligent AI orchestration.