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qwant_suggest

Generate Qwant search-box autocomplete suggestions from a query prefix, returned in Qwant's ranking order; set locale to target a supported market.

Instructions

Suggest Qwant search queries. Returns Qwant search-box autocomplete completions for a query prefix, in Qwant's own ranking order. locale selects the Qwant market (en_US, fr_FR, de_DE, en_GB, ...); Qwant ignores locale codes it does not support and uses its default market.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query prefix
countNoSuggestions to return; defaults to 10, clamped to 1..10
localeNoQwant market locale in ll_CC form, such as en_US, fr_FR, de_DE, or en_GB; defaults to en_US

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.9

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full disclosure burden. It does add real behavioral detail — results come back in Qwant's own ranking order, and unsupported locale codes silently fall back to Qwant's default market. However it says nothing about rate limits, auth requirements, or failure behavior for an unannotated read tool.

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?

Three short sentences, front-loaded with the core purpose, followed by return semantics and the one parameter caveat that matters. No filler or repetition of schema fields.

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?

There is no output schema, and the description compensates by explaining the return set and its ordering. Combined with full schema coverage of all three parameters, an agent has nearly everything needed to call it correctly; only edge-case behavior (empty results, errors) is unaddressed.

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 already 100%, so the baseline is 3, but the description adds operational meaning beyond the schema for `locale`: Qwant ignores unsupported codes and substitutes its default market, which an agent could not infer from the ll_CC description alone. The `q` and `count` parameters gain nothing extra, keeping it short of 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?

States a specific verb (suggest) and resource (Qwant search queries), then clarifies the exact artifact returned: search-box autocomplete completions for a prefix. The Qwant brand naming cleanly separates it from the many other *_suggest siblings (google_suggest, bing_suggest, brave_suggest) 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 autocomplete-prefix framing implies the use case (typeahead completion), but the description never states when to prefer this over a full Qwant search or any sibling suggest tool, nor any exclusions. Usage is inferable rather than directed.

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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