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Estimate a fetch (no call)

liquid_estimate
Read-onlyIdempotent

Estimate the cost of any API fetch before calling it: predicted item count, bytes, tokens, credits, and latency. Decide whether to narrow your request first to save resources.

Instructions

Pre-flight estimate for a fetch — predicted item count, bytes, tokens, credits and latency, each with a confidence and source — without making any HTTP call or LLM call. Read-only and free. Returns {estimate: {...}}. Check this before a potentially large liquid_fetch to decide whether to narrow the pull with liquid_query (filter/aggregate) first. Requires an adapter_id from liquid_connect.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
adapter_idYesAn adapter id returned by liquid_connect (or listed by liquid_list_adapters).
endpointNoOptional endpoint path to act on (e.g. "/users"); defaults to the adapter's primary endpoint. Use a path shown by liquid_connect / liquid_list_adapters.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
estimateNoPredicted items, bytes, tokens, credits, latency with confidence + source.
errorNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already set readOnlyHint and idempotentHint, but the description adds that it makes no HTTP or LLM calls and is free. It also describes the return structure, providing value beyond the structured fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences and a note. It front-loads the key purpose and behavior, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the description covers purpose, usage context, prerequisites, and output structure. The existence of an output schema covers return values, making it complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds context: adapter_id is required from liquid_connect, endpoint is optional with a default. It also explains usage of paths from other tools, adding meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: providing a pre-flight estimate for a fetch without making any calls. It specifies the predicted metrics and distinguishes it from siblings like liquid_fetch and liquid_query.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use the tool (before a large liquid_fetch) and mentions the prerequisite adapter_id from liquid_connect. It lacks explicit when-not scenarios but provides clear context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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