xrm-mcp

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describe_table

Retrieve column names, data types, and descriptions for any Dataverse table to build precise $select and $filter queries.

Instructions

Get columns, types and descriptions for an XRM table.

Call this before querying when you need to know column names for $select or $filter.

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": {
      "org_url": "<org_url>",
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

Name	Required	Description	Default
`table`	Yes	The logical name of the table (e.g., account, cr123_hourentry)
`org_url`	Yes	The Dataverse organization URL

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Jul 5, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It correctly implies a read-only operation ('Get columns, types and descriptions'). However, it does not disclose potential side effects, rate limits, or authorization requirements. The output schema exists, which helps, but more behavioral detail would be beneficial.

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 consists of two concise sentences. It is front-loaded with the primary action and provides usage guidance in the second sentence. No wasted words.

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

Completeness4/5

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

Given the tool's low complexity (2 parameters, both well-documented) and the presence of an output schema, the description is sufficiently complete. It adds usage context beyond the schema, making it effective for an AI agent.

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 baseline is 3. The description adds an example for the 'table' parameter ('e.g., account, cr123_hourentry') and implies the purpose of parameters through context. It does not add extensive meaning beyond the schema but is adequate.

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 states 'Get columns, types and descriptions for an XRM table.' This is a specific verb+resource, and it clearly distinguishes from sibling tools like list_tables (which lists table names) and find_table (which searches for tables).

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

Usage Guidelines4/5

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

The description explicitly advises when to use this tool: 'Call this before querying when you need to know column names for $select or $filter.' It provides clear context for usage, though it does not mention when not to use it or name alternatives.

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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