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hdyrawan

mcp-endpoint-central

by hdyrawan

Get user data type length

custom_column_udt_length
Read-only

Retrieve the maximum character length allowed for a specific custom field's data type. Use this to validate input size before writing data via the ManageEngine Endpoint Central API.

Instructions

Retrieves the maximum number of characters allowed for the user data type of a specific custom field.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableNameYesBacking table of the custom field: ManagedComputerCustomFields for fields created in the Computer view, InvSWCustomFields for fields created in the Software view, MspCustomerCustomFields for MSP customer fields.
actualColumnNameYesThe actual (internal) column name of the custom field, e.g. COL301 — obtain it from the columnName key returned by custom_field_metadata_list.
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds some context about the 'maximum number of characters allowed,' but does not disclose return format, error behavior, or prerequisites beyond what the schema supplies. It meets the baseline but doesn't exceed it.

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 a single, compact sentence that front-loads the verb and resource. It avoids wordiness, though it is fairly terse and could optionally include a note about needing the internal column name (which is already in the schema).

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

Completeness3/5

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

The description and schema together give a clear picture for a simple read-only lookup, and the return value (maximum character count) is implied. However, there is no output schema and no mention of edge cases like missing fields or invalid table names, leaving some gaps for an agent to fully invoke and interpret results.

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 description coverage is 100%, so the schema fully documents both parameters with type, enum, and example. The description adds no additional parameter-level meaning, so the baseline of 3 is appropriate.

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 uses a specific verb 'retrieves' and clearly defines the resource: 'the maximum number of characters allowed for the user data type of a specific custom field.' This distinguishes it from sibling tools that deal with existence checks or listing data types, though it doesn't explicitly name them.

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

Usage Guidelines3/5

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

The description implies when to use the tool (for a specific custom field's type length), but provides no explicit guidance on when to use it over alternatives like custom_column_udt_name_exists or custom_field_metadata_list. There are no exclusions or alternative recommendations.

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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