Classify raw customer support ticket text into its correct category using NLP, so teams can route and prioritize incoming requests accurately.
Classify a support ticket's category (Project 2: NLP text classifier).
Args:
ticket_text: The raw customer support ticket text.
| Name | Required | Description | Default |
|---|---|---|---|
| ticket_text | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It says this is an NLP text classifier, but does not describe output format, returned categories, confidence, permissions, rate limits, or side effects.
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 short and front-loaded with the core action. The parenthetical project identifier adds little, and the Args block is functional but slightly noisy.
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 classifier with one input, no output schema, and no annotations, the description is thin. It does not explain what categories are returned, what the output looks like, or when the tool should be used.
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%, so the description must compensate for the single parameter. It does so by explaining that ticket_text is the raw customer support ticket text, which is more meaningful than the schema's bare 'Ticket Text' title.
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 clear verb and resource: classify a support ticket's category. It also identifies the tool as an NLP text classifier. It does not explicitly distinguish itself from siblings churn_risk_score or policy_question, but the purpose is still 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 gives no guidance on when to use this tool versus alternatives. It does not mention the sibling tools, prerequisites, or conditions that select this classifier over churn risk scoring or policy question answering.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.