language.analyze_sentimentAnalyze ad content sentiment to return score and magnitude, helping assess tone and guide campaign decisions. This calls the Cloud Natural Language API, which incurs usage cost.
Analyze sentiment (score + magnitude) of ad content. Incurs Cloud Natural Language API cost.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It usefully discloses that the tool incurs Cloud Natural Language API cost and states the return dimensions, but it does not mention input limits, quotas, latency, or other operational constraints.
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 short sentences, front-loaded with the core action and followed by the important cost warning. There is no wasted language.
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?
The output schema covers return values and the description notes API cost, which is important for a paid external call. However, for a tool with no annotations, it still lacks parameter constraints and broader operational details that would help an agent invoke it confidently.
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 0% for the single required `text` parameter. The description only implies that the text should be ad content; it does not clarify format, length limits, language expectations, or encoding, so it compensates only minimally for the schema gap.
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: analyze sentiment of ad content. It also names the output dimensions (score + magnitude), which clearly distinguishes it from sibling language tools such as translate, detect_language, and analyze_entities.
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?
Usage is implied by the purpose: use this when sentiment analysis is needed. The cost warning adds a practical consideration, but the description does not explicitly state when to choose this tool over alternatives or when to avoid it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.