Genesys Cloud MCP Server

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

Annotations only provide a title, so the description carries full burden. It describes what is retrieved (topics representing business-level intents) and how they are inferred (from recognised phrases), but does not disclose behavioral traits like rate limits, authentication needs, or response format. No contradiction with annotations exists.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by clarifying details. It uses two efficient sentences with zero waste, each adding value by explaining what topics are and how they are derived.

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

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

Given the tool has 1 parameter with full schema coverage, no output schema, and minimal annotations, the description adequately covers the purpose and nature of topics. However, it lacks details on behavioral aspects like response format or error handling, which could be useful for an agent despite the simple input schema.

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

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

Schema description coverage is 100%, with the single parameter (conversationId) fully documented in the schema. The description does not add meaning beyond the schema, as it does not explain the parameter's role or constraints. Baseline 3 is appropriate since the schema handles parameter documentation.

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

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

The description clearly states the verb ('retrieves') and resource ('Speech and Text Analytics topics detected for a specific conversation'), and distinguishes from siblings by specifying it's about topics (vs. sentiment, transcript, etc.). It provides specific examples of topics (e.g., cancellation, billing enquiry) and explains they are inferred from recognised phrases.

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

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

The description implies usage by specifying 'for a specific conversation' and that topics are inferred from customer-agent interaction, but does not explicitly state when to use this tool vs. alternatives like conversation_sentiment or conversation_transcript. No exclusions or prerequisites are mentioned.

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