chrissaautomates.com
See how AI research agents can decide when to stop, escalate, switch retrieval routes, and avoid unnecessary paid calls using measured benchmark evidence.
First seen 2026-08-31 · Last seen 2026-08-31 · Source: webmcp.com
Answer 1 · Act 1 · Transact 0 · Score: Not yet scored
Tool inspector
recommend_agent_retrieval_strategy
Returns the CAutomates Agent Utility Index evidence-guided web retrieval strategy for a supported AI-agent use case, page workload, and priority. This is read-only and does not call retrieval providers.
Page: /
Input schema
| Property | Type | Required | Description |
|---|---|---|---|
use_case |
string | yes | What the AI system is being built to do. |
workload |
string | yes | Closest benchmarked web-page workload. |
priority |
string | yes | Whether the main goal is fewer paid calls, retrieval quality, or latency. |
Raw JSON schema
{
"type": "object",
"properties": {
"use_case": {
"type": "string",
"enum": [
"lead_enrichment",
"competitive_research",
"product_monitoring",
"rag_research",
"website_audit",
"custom_agent"
],
"description": "What the AI system is being built to do."
},
"workload": {
"type": "string",
"enum": [
"saas_pricing",
"documentation",
"ecommerce_product",
"javascript_heavy",
"difficult_antibot"
],
"description": "Closest benchmarked web-page workload."
},
"priority": {
"type": "string",
"enum": [
"efficiency",
"reliability",
"latency"
],
"description": "Whether the main goal is fewer paid calls, retrieval quality, or latency."
}
},
"required": [
"use_case",
"workload",
"priority"
],
"additionalProperties": false
}
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