Dataverse MCP Server

dataverse_list_columns

Read-onlyIdempotent

List column (attribute) definitions for a Dataverse table.

Instructions

List column (attribute) definitions for a Dataverse table.

Use attribute_type to narrow by column type (e.g., 'Lookup', 'Picklist'). For full metadata on a single column use dataverse_get_column. For Picklist/MultiSelectPicklist option values use dataverse_list_choice_column_options.

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": {
      "params": "<params>"
    },
    "name": "dataverse_list_columns"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.6.0Jul 15, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds useful context about the consistency_strong parameter and its impact on caching, but no additional behavioral traits beyond what annotations cover.

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?

Extremely concise: 5 lines covering purpose, filtering, and alternatives. Front-loaded with primary action, no fluff.

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 existence of an output schema and comprehensive parameter descriptions, the description covers all essential aspects: purpose, filtering via attribute_type, caching behavior, and sibling tool alternatives.

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 descriptions already detail each parameter. Description adds value by explaining usage of 'attribute_type' (common values) and 'consistency_strong' (when to use), but does not repeat schema info.

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?

Description clearly states 'List column (attribute) definitions for a Dataverse table.' It explicitly distinguishes from sibling tools like dataverse_get_column (single column) and dataverse_list_choice_column_options (picklist options), preventing misuse.

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 'attribute_type' to filter, when to use consistency_strong (after metadata changes), and references alternatives for detailed column data or choice options.

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

Deploy Server