Dataddo Data to AI

Known gaps

known_gaps

Read-only

List questions this data CANNOT answer, and why. A question listed here has no answer in this data; a nearby metric answers a different question. An empty inventory is not evidence there are no gaps: gaps are written by hand and derivation produces none.

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": "known_gaps"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No	model name from list_models; omit for the default

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 7, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
First observedOct 6, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare read-only, non-destructive, closed-world behavior, so the safety profile is covered. The description adds real value beyond that: the inventory is hand-written, derivation never produces entries, and an empty result must not be read as proof of no gaps — an important interpretive caveat an agent would otherwise get wrong.

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?

Three short sentences, front-loaded with the purpose and followed by the interpretive caveat; every sentence carries information. The third sentence is slightly compressed in phrasing but not wasteful.

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 no-required-parameter read tool with no output schema, the description conveys enough: what is returned (questions plus reasons), that the list may be empty, and why emptiness is not conclusive. Only the explicit relationship to query-type siblings is left implicit.

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?

There is a single optional parameter and schema description coverage is 100%, so the schema fully documents the model argument. The description adds no further meaning about the parameter, which is acceptable at this coverage level.

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 ('List questions this data CANNOT answer, and why') and immediately distinguishes the concept from a nearby metric that 'answers a different question', so an agent can tell it apart from query/search 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 Guidelines3/5

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

The boundary condition is implied ('a question listed here has no answer in this data') and points at metric tools as the contrasting case, but it never explicitly says when to call this versus query or search, nor any prerequisite ordering such as calling it before concluding data is missing.

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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