Dataverse Plugin MCP

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list_available_messages

List the Dataverse messages a table supports, so you can pass the correct message to create_step() when building a plug-in.

Instructions

List the Dataverse message names (Create, Update, Delete, Assign, ...) supported by a table.

Useful to know what to pass as 'message' to create_step() before attempting to register it.

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": {
      "entity": "<entity>"
    },
    "name": "list_available_messages"
  }
}

Input Schema

Name	Required	Description	Default
`entity`	Yes
`environment`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 1, 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 disclosure burden. It clearly implies a read-only operation ('List') and provides practical context about its role in create_step, but it does not mention permissions, error behavior, environment effects, or other side effects. The description is adequate but not rich in behavioral details.

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 is just two short sentences. The first sentence states the operation and scope, and the second provides practical usage context. There is no redundant filler or repetition of the tool name, making it concise and well-structured.

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?

The tool has an output schema, so return values need not be explained in the description. Given the simple listing purpose and the useful note about create_step, the description covers the essential context. However, it omits any guidance about the environment parameter and does not discuss alternatives, so it is not fully complete.

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 0%, so the description must compensate. It explains the role of the 'entity' parameter by referring to 'a table' and clarifies that the output is intended as the 'message' argument for create_step(). However, the optional 'environment' parameter is not mentioned at all, leaving a semantic gap.

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 the specific verb 'List' and identifies a clear resource: 'Dataverse message names ... supported by a table,' with concrete examples (Create, Update, Delete, Assign). It also distinguishes this from sibling tools by explicitly connecting the output to create_step(), making the tool's purpose unambiguous.

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 second sentence directly states when to use this tool: 'Useful to know what to pass as message to create_step() before attempting to register it.' This provides clear contextual guidance for the agent. It does not explicitly mention alternatives or when not to use it, but the workflow-oriented instruction is strong.

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