CustomerIQ-Agent
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| churn_risk_scoreB | Look up a customer's churn risk score (Project 1: tabular ML model). |
| support_ticket_categoryB | Classify a support ticket's category (Project 2: NLP text classifier). |
| policy_questionB | Answer a question grounded in company policy documents (Project 3: RAG). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool targets a clearly distinct task: churn risk scoring, support ticket classification, and policy Q&A. There is no overlap in purpose or inputs, so an agent can easily select the correct tool.
All names use consistent snake_case and are descriptive noun phrases, which is readable and predictable. However, they do not follow the common verb_noun action pattern, so the convention is consistent but not action-oriented.
Three tools map cleanly to three distinct underlying capabilities (tabular ML, NLP classifier, RAG). The count is well-scoped and each tool earns its place without redundancy.
The surface covers the three stated project functions, but lacks supporting operations like customer lookup, ticket history, or score explanation. These are minor gaps that an agent could work around for the core tasks.