Dataddo Data to AI

Connected data

list_flows

Read-only

Answers "what business data do we have", "what are we connected to" and "what systems feed this account": lists the Dataddo FLOWS this account is connected to and which ones a model covers, so they can actually be queried. Each flow reports how many columns it has, not the columns themselves — call describe_flow for one flow's inventory. Check columns_from: source_row_count and source_column_count describe the SOURCE behind a flow and are upper bounds, because a flow may filter rows or emit fewer columns. What those columns MEAN still requires a model: a flow with queryable=false is connected and inspectable, not answerable; list_entities has what can be asked. Returns a page: 20 by default, at most 200, with counts.matched_filter giving the true total — advance 'offset' rather than concluding the account ends there. A page can come back SHORTER than you asked when the response budget trims it; 'flows_truncated' says so, and you then advance by the number you RECEIVED, never by the limit you requested.

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_flows"
  }
}

Input Schema

Name	Required	Description
`only`	No	'modelled' for flows a model covers, 'unmodelled' for the rest; omit for all
`type`	No	omit for the default, which is 'fetch' — the Smart Cache flows modelled automatically. 'write' for flows pushed to an external system (not modelled automatically), or 'any' for every flow regardless of type
`limit`	No	maximum flows to return; default 20, maximum 200
`offset`	No	skip this many flows before listing; use with the reported match count to page past the limit
`contains`	No	case-insensitive filter on flow label or source name

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 10, 2026

changedInput schema / properties / type / description

Previous value: -"omit for the default, which is 'fetch' — the Smart Cache flows this layer can actually read. 'write' for flows pushed to an external system (not readable here), or 'any' for every flow regardless of type"New value: +"omit for the default, which is 'fetch' — the Smart Cache flows modelled automatically. 'write' for flows pushed to an external system (not modelled automatically), or 'any' for every flow regardless of type"

Changed2 schema fields changedOct 7, 2026

addedInput schema / additionalProperties
```
Added value: +false
```

changedInput schema / properties / type / description

Previous value: -"omit for the default, which is 'fetch' - the Smart Cache flows this layer can actually read. 'write' for flows pushed to an external system (not readable here), or 'any' for every flow regardless of type"New value: +"omit for the default, which is 'fetch' — the Smart Cache flows this layer can actually read. 'write' for flows pushed to an external system (not readable here), or 'any' for every flow regardless of type"

First observedOct 6, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and non-destructive, so the bar is lower, but the description adds substantial behavior: page budget can return fewer rows than requested, flows_truncated signals it, and offset must advance by rows received rather than the limit. It also warns that source_row_count/source_column_count are upper bounds because flows may filter rows or emit fewer columns.

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?

Front-loaded with the purpose and the sibling routing, then paging caveats. Dense but each clause carries operational meaning (truncation, counts, queryable=false); the opening list of three paraphrased user questions is slightly redundant but sets scope quickly.

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?

For a read-only list tool with no output schema, the description covers the return shape (per-flow column counts, counts.matched_filter, flows_truncated), paging semantics, and cross-tool next steps. An agent has everything needed to call it correctly and interpret the response without guessing.

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 coverage is 100%, so baseline is 3; the description still adds paging discipline (advance offset using counts.matched_filter, never assume the account ends at one page) and clarifies that limit is capped at 200 by default 20. The 'contains' filter and enum-less 'only'/'type' values are mostly restated from the schema, keeping it below a 5.

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?

Names a specific verb and resource (lists the Dataddo FLOWS this account is connected to) and frames it against the user questions it answers. It explicitly separates itself from describe_flow (column inventory) and list_entities (what can be asked), so an agent can route without opening a schema.

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?

Gives explicit routing rules: call describe_flow for one flow's columns, list_entities for answerable questions, and it explains that a flow with queryable=false is not answerable. It also states when to use the 'only' filter values and that omitting 'type' defaults to 'fetch'.

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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