APIVerve Certified Developer: AI Agents

• Explain tools, resources and prompts, and what a client and a server each own
• Connect the APIVerve MCP server to an editor or desktop client and prove it works
• Read input and output schemas, structuredContent and null or premium fields correctly
• Give each agent its own scoped sub-key with a bounded spend
• Treat tool results as untrusted input and keep prompt injection away from your actions

Study: MCP Fundamentals · 4 lessons

• Decide which questions an agent must answer with a tool instead of from memory
• Design a small, well-named tool set and narrow wrappers instead of exposing raw APIs
• Return tool errors the model can act on, and stop an agent loop on fatal ones
• Cache, parallelise and budget tool calls so each conversation has a known cost
• Measure tokens, credits and latency per turn with real numbers

Study: Ground Agents in Live Data · 4 lessons

• Generate tool definitions from an API's published parameters and run the tool-use loop server-side
• Build a research agent that reads pages, summarises them and cites only sources it actually read
• Build a triage agent that gathers verification evidence and writes a recommendation for a human
• Test agents against a fixed case set, checking which tools were called and with what arguments
• Add budgets, input validation, human approval and structured logs before an agent goes live

Study: Build Agent Workflows · 4 lessons