Skip to main content
Glama

laserfiche_field_definition_list

List every field definition in a Laserfiche repository to discover field types, multi-value support, required status, and allowed values for List fields. Use before authoring field-based queries or preparing field updates.

Instructions

List every field definition in the repository.

Use before authoring a field-based search query or preparing a field update — the response tells you which fields exist, their types (String, ShortInteger, List, Date, ...), whether they accept multi-value, whether they're required at the repository level, and (for List fields) the allowed values.

Independent fields and template-scoped fields are both returned. Combine with list_template_definitions to see which fields belong to which template.

Args: max_results: Page size (default 25, capped by LF_MAX_RESULTS_CEILING). skip: 0-indexed offset for pagination through large repositories. summary_only: If True, return only {count, names} instead of the full OData listing.

Returns: Server's raw OData listing with value (list of field definitions). Each item includes id, name, fieldType, isRequired, isMultiValue, listValues, defaultValue, length, constraint.

On failure: returns {"mode": "error", "error": <slug>, ...}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_resultsNoPage size (default 25, capped by LF_MAX_RESULTS_CEILING).
skipNo0-indexed offset for pagination through large repositories.
summary_onlyNoWhen True, return only {count, names} instead of the full OData listing — useful for 'what's available?' lookups that would otherwise return 30-50 KB of definition payload.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description fully discloses behavior: it returns a raw OData listing with specific fields, describes pagination (max_results, skip), the summary_only option, and failure mode ('On failure: returns error...'). 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?

Well-structured: opening sentence, usage context, parameter details with Args, return format, and failure mode. Each sentence is informative without redundancy. Front-loaded with the main purpose.

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 has 3 parameters and an output schema, the description covers purpose, usage context, all parameters, return format (including fields and error structure), and relationships with other tools. No gaps for an AI agent to invoke 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%, so baseline is 3. The description adds value by explaining the purpose of summary_only (reduces payload for quick lookups) and noting that max_results is capped by LF_MAX_RESULTS_CEILING, which is not in the schema description.

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 it lists every field definition in the repository, specifies the types of information returned (field names, types, multi-value, required, list values), and distinguishes from siblings by mentioning 'Combine with list_template_definitions' to show template membership.

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?

Explicitly states when to use: 'before authoring a field-based search query or preparing a field update.' Also suggests combining with list_template_definitions for template-scoped fields, providing clear guidance on tool selection.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/SamuelSHernandez/laserfiche-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server