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Argorant

by com.argorant
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Check several exports

argorant_export_batch_status
Read-onlyIdempotent

Show how far an export that was split into several files has come, with the state of each file. Use it with the batch_id argorant_create_export gives for a large search. For a single file use argorant_export_status. It is free, starts no new export and sends no email. More in the docs at https://docs.argorant.com/docs/agents/tools#reveal-enrich-export

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "batch_id": "<batch_id>"
    },
    "name": "argorant_export_batch_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
batch_idYesThe batch_id from argorant_create_export.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered structurally. The description adds genuinely new behavioral context — the call is free, starts no new export, and sends no email — which reassures an agent that polling is harmless. It stops short of describing polling cadence or terminal states, which is the only remaining gap.

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

Conciseness4/5

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

Purpose is front-loaded in the first sentence, followed by the required input source and the sibling alternative, which is good ordering. The docs URL at the end is minor padding but does not obscure anything.

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?

With an output schema present and rich annotations, the description need not explain return values, and it covers purpose, inputs, alternatives, and side effects. For a 1-parameter status tool this is essentially complete; only polling expectations are unstated.

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

Parameters3/5

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

Schema coverage is 100% and the single parameter is already documented as coming from argorant_create_export. The description restates that provenance and adds the 'large search' qualifier, but supplies no format, range, or error-handling detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb and resource: shows progress of an export split into several files, including the state of each file. It explicitly distinguishes itself from the single-file sibling argorant_export_status, so an agent can choose between them without opening either schema.

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

Usage Guidelines5/5

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

Gives the exact trigger (a large search export created by argorant_create_export) and the required batch_id provenance, plus an explicit alternative for the single-file case. Nothing about when to select this tool is left to inference.

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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