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laserfiche_template_field_list

Get a list of fields for a Laserfiche template with metadata including type, constraints, and required status. Filter to required-only fields to determine minimum input needed before assigning a template.

Instructions

Return the fields belonging to a single template, with full field metadata.

Closes the most common pre-assign workflow gap: instead of fetching list_template_definitions then list_field_definitions and cross-referencing client-side, this returns the template's field list directly with each field's type, constraints, and required flag inlined. Use this BEFORE assign_template to construct the fields argument.

Args: template_name: Exact template name (case-sensitive on most builds). Use list_template_definitions to discover available names. required_only: When True, return only fields where is_required is true. Useful for "what's the minimum I have to supply?" workflows.

Returns: {"template_name": <str>, "template_id": <int>, "field_count": <int>, "fields": [...]} where each field has name, field_type, is_required, is_multi_value, list_values, default_value, length, constraint.

On failure: returns {"mode": "error", "error": <slug>, ...}. Slugs: invalid_template_name when the template name doesn't exist in the repository (with the list of valid names in the response); server_error for upstream issues.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
template_nameYesExact template name (case-sensitive on most builds). Use list_template_definitions to discover available names.
required_onlyNoWhen True, return only fields where is_required is true — useful for 'what's the minimum I have to supply?' workflows.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

No annotations are provided, but the description fully compensates by detailing return structure (including failure modes with specific error slugs), case-sensitivity of template names, and optional parameter behavior. No contradictions with annotations (none exist).

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?

The description is front-loaded with the core purpose, then follows a logical structure (workflow gap, args, returns). It is detailed yet not excessively long; every sentence adds value. Slight redundancy in return structure but overall well-organized.

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?

The description fully covers the tool's purpose, parameters, return values, error handling, and workflow context. Given the presence of an output schema (described), it leaves no gaps for an AI agent to function correctly.

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% and both parameters are well-described. The description adds additional context like examples for template_name and practical use case for required_only, providing value beyond the schema alone.

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 'Return the fields belonging to a single template, with full field metadata', using a specific verb and resource. It distinguishes itself from siblings like list_template_definitions and list_field_definitions by highlighting the workflow gap it fills.

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?

Provides explicit guidance: 'Use this BEFORE assign_template to construct the fields argument' and contrasts with alternatives 'instead of fetching list_template_definitions then list_field_definitions'. Clearly marks when and why to use this tool.

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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