YarnGPT MCP by Nova (CIVAI)

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

With no annotations, the description still discloses the payload (voice names plus descriptions) and the operational sequencing relative to text_to_speech, so the agent knows this is a cheap read used for discovery. It does not say whether the list is static, whether it can fail, or what the notification_message parameter does on failure, which keeps it short of a 5.

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?

Two tight sentences front-load the purpose and routing rule. The trailing enumeration of 16 voice names is the only questionable content: it duplicates what the tool returns and could go stale, though it lets the agent answer basic questions without a call.

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?

There is no output schema, so the description correctly carries the return shape (names and descriptions) — that burden is met. For a zero-required-parameter read tool with no annotations, the remaining gaps (failure behavior, list freshness) are minor.

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% and the single parameter (notification_message) is fully documented in the schema, so the baseline is 3. The description adds nothing about that parameter — notably it does not explain how a fallback notification relates to a list operation — but there is no gap to compensate for.

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?

States a specific verb and resource ("List all available YarnGPT voices") and immediately says what the result contains (names and descriptions). It is clearly distinguishable from the only sibling, text_to_speech, which synthesizes speech rather than enumerating voices.

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?

Explicit routing: "Use before text_to_speech when the user asks which voices exist or which voice to pick." It names the alternative tool and the exact user intents that select this one, leaving nothing to inference.

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

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

With no annotations, the description carries the full disclosure burden. It usefully mentions the API key source, max text length, and default output format, but it does not describe what the tool returns, whether generation is synchronous, or any cost/rate-limit behavior.

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 compact and front-loads the core action and required inputs. The only wasted or confusing clause is the response_format mention, which refers to a parameter not present in the input schema.

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?

For a simple generation tool with rich schema descriptions, the definition covers key operational details such as auth source and voice discovery. However, with no output schema, it leaves the return behavior unclear and includes a response_format detail that does not match the actual 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%, so the schema already defines all three parameters. The description repeats the text and voice requirements and adds some context, but it also mentions response_format even though that parameter is absent from the schema, while omitting notification_message.

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 states a specific verb and resource: convert text to speech using YarnGPT. It distinguishes the tool from its sibling by telling the agent to use list_voices for voice discovery, so the purpose is immediately clear.

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 explains required inputs, the character limit, default audio format, voice discovery via list_voices, and that the API key comes from global_variables. It gives clear usage context, though it does not specify when not to use this tool.

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