Rightsize AI Context

This use case focuses on exposing only the context an AI task needs—reducing noise, improving relevance, and keeping prompts efficient. When AI agents receive everything an API returns, context windows fill with irrelevant data, prompts bloat with unnecessary tokens, and model accuracy suffers.

Teams need fine-grained control over what data reaches AI clients. Typed output parameters with JSONPath mapping expressions shape response payloads so only relevant fields appear in exposed tool and resource schemas. Meaningful descriptions, documentation, and tags enable semantic discovery and maintain context quality across the catalog.

Pain Points

• AI agents receive entire API responses when they only need a subset of fields
• Bloated context windows waste tokens and increase costs
• Irrelevant data in prompts leads to hallucination and unpredictable behavior
• No standard way to filter or shape API responses for AI consumption
• Tool and resource schemas lack descriptions needed for semantic discovery
• Teams cannot measure or improve context quality across capabilities

Expected Outcomes

• Only relevant fields exposed to AI clients through typed output parameters
• Smaller prompts with fewer tokens and better model accuracy
• Rich metadata enabling semantic discovery across the capability catalog
• Predictable agent behavior through schema-validated, right-sized payloads
• Measurable context quality across capabilities

Narrative

An organization deploys MCP servers on top of existing APIs, and initial results are mixed. Agents receive full API responses—dozens of fields when they need three or four. Context windows fill quickly, token costs climb, and model accuracy degrades as irrelevant data pollutes prompts.

Teams begin shaping response payloads with typed output parameters and fine-grained JSONPath mapping. Each capability exposes only the fields an AI task actually needs, while hiding irrelevant upstream data from the AI surface. The immediate result is smaller prompts, fewer tokens, and more predictable agent behavior.

Beyond field filtering, teams attach meaningful descriptions, documentation, and tags to every tool and resource. These metadata layers enable semantic discovery—agents and developers find the right capability based on what it does, not just what it is called. Context quality becomes visible and measurable across the catalog.

Right-sized context is not a one-time cleanup. As APIs evolve and new capabilities are added, the same declarative approach ensures that AI clients always receive precisely what they need—no more, no less. The organization moves from raw API dumps to a governed, efficient context delivery surface.