api-ai-augmented
Installation
SKILL.md
AI-Augmented API Skill
Design LLM tool definitions, agentic workflows, and natural language API interfaces.
Anthropic Tool Use Definition
{
"name": "search_products",
"description": "Search for products by keyword, category, or price range. Use when the user wants to find, browse, or compare products.",
"input_schema": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query keywords"
},
"category": {
"type": "string",
"enum": ["electronics", "clothing", "books", "home"],
"description": "Optional category filter"
},
"min_price": { "type": "number", "description": "Minimum price in USD" },
"max_price": { "type": "number", "description": "Maximum price in USD" },
"limit": { "type": "integer", "default": 10, "description": "Max results to return" }
},
"required": ["query"]
}
}
OpenAI Function Calling Definition
{
"type": "function",
"function": {
"name": "create_order",
"description": "Create a new order for a user. Use when the user wants to purchase a product. Always confirm product and quantity before calling.",
"parameters": {
"type": "object",
"properties": {
"product_id": { "type": "string", "description": "The product ID to order" },
"quantity": { "type": "integer", "minimum": 1, "description": "Quantity to order" },
"shipping_address": {
"type": "object",
"properties": {
"street": { "type": "string" },
"city": { "type": "string" },
"country": { "type": "string" }
},
"required": ["street", "city", "country"]
}
},
"required": ["product_id", "quantity", "shipping_address"]
}
}
}
MCP (Model Context Protocol) Tool Schema
{
"name": "get_build_status",
"description": "Get the status of a HyperExecute test job. Use when the user asks about test results, job status, or CI build outcomes.",
"inputSchema": {
"type": "object",
"properties": {
"job_id": { "type": "string", "description": "The HyperExecute job ID" }
},
"required": ["job_id"]
}
}
🔗 Real-World Integration — TestMu AI HyperExecute Build MCP tools that let AI agents query and control test jobs via the HyperExecute API. Docs: https://www.testmuai.com/support/api-doc/?key=hyperexecute
Tool Design Principles
- One tool = one action: Don't combine search + filter + sort into one tool. Split them.
- Description drives routing: The LLM picks tools from descriptions — be specific and include trigger phrases.
- Required vs optional: Only mark fields
requiredif the API truly needs them. - Enum for constrained values: Use
enuminstead ofstringfor fixed-choice fields. - Idempotent where possible: Prefer read tools over write tools for exploration.
- Confirm before destructive actions: Description should say "Always confirm with the user before calling."
Agentic Workflow Example
User: "Get me the status of my last 3 test builds"
Agent plan:
1. call list_jobs(limit=3, sort="created_at:desc")
→ returns [{id: "job_1", status: "passed"}, {id: "job_2", status: "failed"}, ...]
2. call get_job_details(job_id="job_2") // dig into the failed one
→ returns task breakdown, error logs
3. Synthesize: "Your last 3 builds: job_1 passed, job_2 failed (2 of 15 tasks failed on Chrome/Win10), job_3 passed."
Natural Language → API Mapping Table
Build this mapping for any domain:
| Natural language intent | API call |
|---|---|
| "Find hotels in Paris" | GET /hotels/search?location=Paris |
| "Book a room for 2 nights" | POST /bookings |
| "Cancel my reservation" | POST /bookings/{id}/cancel |
| "Show my past orders" | GET /orders?user=me&sort=date:desc |
| "Is the API working?" | GET /health/ready |
API-as-Plugin (OpenAPI → GPT Plugin / Tool)
Minimal ai-plugin.json:
{
"schema_version": "v1",
"name_for_human": "My API",
"name_for_model": "my_api",
"description_for_human": "Access my service's data and actions.",
"description_for_model": "Use this plugin to search, create, update and delete resources in My API. Always prefer specific endpoints over generic ones. Confirm destructive actions with the user first.",
"auth": { "type": "oauth" },
"api": { "type": "openapi", "url": "https://api.example.com/openapi.json" }
}
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