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export_calls

Export large batches of LLM call records with filters by time, provider, or model. Returns JSON data for analysis or conversion to CSV.

Instructions

Large-batch export of calls (POST /v1/query/export). Higher limit than query_calls (per-plan max records: Free 1000 / Pro 50000); available on all plans. Filter axes = startTime / endTime / provider / model plus limit. Example phrasing: "pull all of last month's GPT-4 calls and analyze the trends" — one call. The result format is the same JSON as query_calls (the AI can feed it straight into CSV / statistics).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoCap on returned records. Passed through when within the plan max; clamped to the plan max beyond it
modelNoModel name filter (exact match, no substring matching; e.g. 'gpt-4o-mini')
endTimeNoRange end ISO timestamp (UTC; omit = now)
providerNoProvider filter (openai / anthropic / google / azure / cohere)
startTimeNoRange start ISO timestamp (UTC; omit = all time)
Behavior4/5

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

With no annotations, the description fully handles behavioral disclosure. It explains the export operation, plan-specific limits, limit clamping behavior, and that the result format matches query_calls. This provides adequate transparency without contradictions.

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—three sentences with no wasted words. It front-loads the purpose and key differentiator, then adds necessary details (limits, filters, example). Every sentence earns its place.

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

Completeness4/5

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

Given 5 parameters, 100% schema coverage, no output schema, and sibling tools like query_calls, the description is sufficiently complete. It mentions the result format and provides guidance on limits and filters, covering essential context for correct usage.

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 description coverage is 100% (all parameters described). The description adds value by explaining the 'limit' parameter's clamping behavior and summarizing filter axes, which goes beyond the schema's individual descriptions.

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: 'Large-batch export of calls (POST /v1/query/export).' It distinguishes from the sibling query_calls by highlighting a higher limit and per-plan maximums, providing a specific verb+resource+scope.

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?

The description explicitly compares to query_calls, noting 'Higher limit than query_calls' and per-plan record limits. It provides filtering axes and an example usage, offering clear context for when to use this tool instead of alternatives. No explicit when-not-to-use, but the comparison suffices.

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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