agentweb-mcp

suggest

Resolve incomplete user input with real-time autocomplete suggestions for business names, cities, or categories. Quickly disambiguate queries like 'Copen' to 'Copenhagen' before searching.

Instructions

Autocomplete / type-ahead for business names, cities, or categories. Use this when you need to disambiguate user input before searching — e.g. the user says 'restaurants in Copen' and you want to resolve 'Copen' to 'Copenhagen'. Returns up to 15 suggestions sorted by relevance. Fast (<20ms).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "q": "<q>"
    },
    "name": "suggest"
  }
}

Input Schema

Name	Required	Description	Default
`q`	Yes	Query prefix, min 2 characters (e.g. 'nom', 'copenh', 'dent')
`type`	No	What to autocomplete: 'business' (names), 'city' (city names), 'category' (business categories). Default: 'business'.	business
`country`	No	Optional ISO country code to narrow suggestions (e.g. 'DK', 'US')
`limit`	No	Max suggestions to return (default: 10, max: 15)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.17.0Jun 21, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations provided, the description bears full responsibility. It discloses that the tool returns up to 15 suggestions sorted by relevance, and is fast (<20ms). This covers key behavioral traits such as return limit, sorting, and performance. It does not mention authentication or side effects, but for a read-only autocomplete, these are acceptable gaps.

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 composed of three concise, front-loaded sentences. The first sentence states the purpose, the second explains when to use with an example, and the third gives actionable return details. No superfluous content; every sentence serves a purpose.

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?

Given the tool's low complexity, the description covers the core aspects: purpose, use case, input constraints (minimum 2 chars for 'q', optional country and limit), and output behavior (max 15 results, sorted by relevance). It does not detail output format or error handling, but these are reasonable omissions for a straightforward autocomplete. The description is sufficiently complete for an AI agent to use the tool effectively.

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?

Schema description coverage is 100%, so parameters are already well-documented. The description adds context by summarizing the use case and providing an example that implies the 'q' parameter usage. However, it does not add significantly new information beyond the schema definitions; the baseline of 3 is appropriate.

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 clearly states the tool is for autocomplete/type-ahead of business names, cities, or categories. It provides a concrete example ('Copen' to 'Copenhagen') and distinguishes from sibling search tools by positioning it as a disambiguation step before searching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

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

The description explicitly says 'Use this when you need to disambiguate user input before searching' and gives an example. This clearly indicates when to use the tool, though it does not explicitly list when not to use or name sibling tools. The context is strong enough for proper selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.