---
title: "Reading your logs and querying them programmatically"
canonical_url: https://coolhandlabs.com/help/category/logs-and-api
index_url: https://coolhandlabs.com/help.md
---

# Reading your logs and querying them programmatically

What every field on an LLM request log means, when batching is worth it, and how to query your observability data from an AI agent over MCP.

## What every field on an LLM request log means

Every LLM request log records the provider, model, prompts, response, token counts, cost, latency, and outcome. Some fields are structurally unavailable for certain providers rather than missing by mistake, so this reference covers what each field means and who leaves it blank.

https://coolhandlabs.com/help/llm-request-log-fields.md

## When should I use batch requests?

Batch APIs process a set of independent LLM requests asynchronously, usually within 24 hours, at roughly half the per-token price. They suit any workload where nobody is waiting on the result. Coolhand flags templates whose traffic pattern suggests batching would cut cost without hurting quality.

https://coolhandlabs.com/help/how-to-use-batch-requests.md

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