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ai_parser_get_details

Read-onlyIdempotent

Check an AI parser's status, fields, and errors. Poll after create or update until status is FINISHED.

Instructions

Get an AI parser's details and generation status.

    Returns the parser's status ('GENERATING', 'FINISHED', or 'FAILED'), its
    fields, example results, and any error. Poll this after 'ai_parser_create'
    (or after a field-editing 'ai_parser_update') until status is 'FINISHED'.

    When to use:
    - Polling a parser's status after create/update until it is 'FINISHED'
    - Inspecting a parser's fields before parsing with it

    When NOT to use:
    - Extracting data from a page (use 'ai_parser_parse_url')

    Args:
        params (AiParserGetParams): parser_id (required) and optional version.

    Returns:
        str: JSON with the parser details and status.

    Raises:
        ToolError: If the API key is missing, the rate limit is exceeded, or
            the request fails.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context: returns status values (GENERATING, FINISHED, FAILED), fields, example results, errors, and the need to poll until FINISHED. It also mentions potential ToolError conditions (missing API key, rate limit, failure). This aligns with and enriches the annotations, no 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 well-structured with a clear summary, separate sections for usage, args, returns, and raises. Every sentence adds value without redundancy. It is front-loaded with the primary purpose and polls status information, making it easy to scan.

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 simple parameter set (1 required param), comprehensive annotations (readOnly, idempotent), and presence of an output schema (though not detailed in description), the description covers all necessary aspects: purpose, usage, behavior, errors, and return value type. It is complete for the tool's complexity.

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

Parameters2/5

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

Context indicates schema description coverage is 0%, meaning the schema lacks parameter descriptions. The description only briefly mentions 'parser_id (required) and optional version' without adding further meaning (e.g., format, constraints, or usage nuances). It does not compensate adequately for the missing schema descriptions, leaving the agent underinformed.

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 gets an AI parser's details and generation status. It explicitly distinguishes from siblings by mentioning polling after create/update and inspecting fields before parsing, and by saying not to use for extracting data (use ai_parser_parse_url). The verb 'Get' and resource are clear.

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?

The description has dedicated 'When to use' and 'When NOT to use' sections. It specifies polling after create/update, inspecting fields before parsing, and explicitly warns against using for data extraction, pointing to ai_parser_parse_url as the alternative. This provides excellent guidance.

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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