DatoQA

list_sources

Read-onlyIdempotent

Inspect publishers and licenses in the DatoQA-approved source registry. Returns items with source id, name, homepage_url and license, ordered by id, up to limit (default 50, maximum 100); no pagination. Use for attribution or source selection, not data retrieval or freshness checks. Use list_topics to choose a data tool and catalog for service capabilities. Existing data responses already include citations; this extra call is optional. Approval is an internal source policy, not independent factual verification. An empty list means no approved source entries were returned.

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_sources"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum source entries returned, default 50.

Output Schema

Name	Required	Description	Default
`items`	Yes
`limit`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedSep 28, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

First observedSep 27, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description adds substantial extra context beyond them: ordering by id, no pagination, empty-list semantics, and the important caveat that 'approval is an internal source policy, not independent factual verification.' That last point is a genuine behavioral disclosure an agent could not get from the schema.

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?

The purpose and return shape are front-loaded, and each clause carries information. It is on the longer side and repeats the default-50 limit slightly, but nothing is filler.

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 a full schema, an output schema, and complete annotations, this description covers scope, output fields, ordering, pagination limits, alternatives, and result semantics. An agent has everything needed to call it correctly.

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% for the single 'limit' parameter (default 50, max 100), so the baseline is 3. The description reinforces default/maximum and adds the behavioral note that there is no pagination, which meaningfully shapes how the limit should be chosen.

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?

States a specific verb and resource ('Inspect publishers and licenses in the DatoQA-approved source registry') and enumerates the returned fields, so an agent knows exactly what it yields. It is clearly distinguishable from data-fetching siblings like get_record or get_evidence.

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?

Explicitly gives when-not ('not data retrieval or freshness checks'), when-to-use alternatives ('Use list_topics to choose a data tool and catalog for service capabilities'), and even notes the call is optional since responses already include citations. Nothing is left to inference.

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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