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mundurragacl

Amazon Connect MCP Server

by mundurragacl

ai_search_quick_responses

Search a knowledge base for quick responses by providing a query text and knowledge base ID. Retrieve relevant pre-written answers to address customer inquiries.

Instructions

Search quick responses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_textYes
max_resultsNo
knowledge_base_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Search quick responses.' gives no details about read-only guarantees, pagination, authentication, or what kind of search is performed. It implies a read operation but does not state it explicitly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no wasted words, and the purpose verb is front-loaded. However, it is under-specified: it is too brief to compensate for the missing parameter and usage information, though it is not a total tautology like 'Process'.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This search tool has three parameters and an output schema, but the description gives no context about what 'quick responses' are, how search behaves (e.g., fuzzy vs exact), or how parameters refine results. The output schema exists but does not compensate for the lack of behavioral and semantic context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage and the description does not mention any of the three parameters (query_text, max_results, knowledge_base_id). The description adds no meaning beyond the parameter names themselves, leaving the agent to guess how the parameters interact.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Search') and a clear resource ('quick responses'), so the basic function is clear. However, it does not distinguish this from the sibling tool 'ai_list_quick_responses', and does not specify any scope or filtering behavior.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the similarly named 'ai_list_quick_responses' or other search tools. The description only states the action without providing context, prerequisites, or exclusions.

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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