Need Task-Oriented MCP Tools
Nico sees MCP servers exposing every API option when agents only need task-specific subsets, wasting context and causing confusion.
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Persona Story:
Nico, the partner AI lead, sees MCP servers exposing every API option when agents only need task-specific subsets, wasting context and causing confusion.
Problem Context
- Current approach: expose each API endpoint as an MCP tool
- AI sees full schema including options it doesn’t need for the specific task
- Huge context waste makes AI “forget other things faster”
Problem Impact
- Context window consumed by irrelevant options
- AI makes worse decisions with more noise
- Inefficient use of expensive token budgets
Naftiko Today
- Agent Skills exposure groups MCP tools into task-oriented skill bundles, so agents only load the tools relevant to a specific business task rather than every API endpoint
- outputParameters normalization ensures agents see only the fields they need with clean names, not the full upstream API schema
- Multi-step orchestration composes multiple API calls into a single task-oriented MCP tool, replacing many fine-grained tools with one purpose-built capability
- Raw API payloads never reach the LLM — the outputParameters governance layer strips irrelevant data before it enters the context window
Naftiko Tomorrow
- Tool annotations for readOnly/destructive/idempotent (Second Alpha) will let agents filter tools by safety profile, further reducing irrelevant options in context
- Dynamic context scoping (Future) will allow agents to load only the subset of tools relevant to the current task at runtime
- Conditional steps with if/for-each (Second Alpha) will enable single tools that handle branching logic internally rather than exposing multiple tools for each branch
- Mock mode (Second Alpha) will let teams test task-oriented tool designs against agents without backend connectivity, validating context efficiency before deployment