Dataddo Data to AI

Available data areas

list_models

Read-only

List the semantic models available — each covers one source system and contains MANY entities — and which is the default. (A model here is a whole semantic layer, not a single table as in dbt.) Use it when you are about to ASK something and need to know which world to ask in. Models cover only the part of the account that has been modelled; what the account is connected to is list_flows.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_models"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 7, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
- removedInput schema / properties
```
Removed value: -{}
```
First observedOct 6, 2026

TDQS

A4.6/5.0

Behavior4/5

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

The annotations already establish that this is a safe, read-only, non-destructive, closed-world operation, so the description does not need to restate that. It adds useful behavioral context by explaining what a model is, that the list is scoped to modelled parts of the account, and that the default model is included, though it does not describe ordering, empty results, or exact return fields.

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 front-loaded with the core action and resource, then adds only the clarifications needed to distinguish semantic models from dbt tables and from list_flows. Every sentence carries useful information, and the parenthetical clarification is placed where it prevents misinterpretation.

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?

For a zero-parameter listing tool, the description covers purpose, scope, default-model behavior, and the main sibling alternative, which is enough to invoke it correctly. Because there is no output schema, the exact response shape is not fully specified, but the high-level return meaning is clear.

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?

The tool has zero input parameters, so there are no parameter meanings for the description to clarify. The baseline for a parameterless tool is 4, and there is no parameter-related gap to compensate for.

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 gives a specific verb and resource: it lists semantic models, explains that each covers one source system and contains many entities, and explicitly says which model is the default. It also distinguishes the term from dbt tables and from list_flows, so an agent can differentiate it from siblings without opening schemas.

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?

It explicitly says to use this when you are about to ASK something and need to know which world to ask in, which is a clear usage condition. It also names list_flows as the alternative for seeing what the account is connected to, giving both when-to-use and when-to-use-something-else guidance.

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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