CJJones/ServiceNow_Search_NLP_2_JSON_LLM_Training_Sample

This dataset contains structured User → Bot conversations demonstrating how a natural language request can be translated into a structured ServiceNow incident search API call.

The full CJ Jones' synthetic dataset catalog is available at: https://datadeveloper1.gumroad.com

Each record consists of a user requesting incident data from an IT service management system and a bot responding with a JSON query specification compatible with the ServiceNow Table API.

The dataset is designed for training and evaluating LLM tool-use capabilities, specifically:

Natural language → API query translation

Incident ticket search automation

IT service desk assistant systems

Enterprise workflow copilots

Retrieval query generation

The bot responses strictly follow a structured schema that represents a ServiceNow incident table search operation.

This dataset preview includes 10 example conversation pairs.

Supported Tasks

tool-use

function calling

information extraction

enterprise automation

natural language query translation

retrieval query generation

Languages

English

Dataset Structure

Each sample contains two fields:

{ "user": string, "bot": string } Field Descriptions Field Description user Natural language request asking for incident or ticket history bot Structured JSON tool call specifying the ServiceNow search parameters Example User: Could you get trouble tickets that mention API integration failures regarding Seattle office staff ranking 2 for post-mortem review.

Bot: { "action": "servicenow.table.search", "parameters": { "tableName": "incident", "query": { "short_description": "CONTAINS: API integration failures", "location": "Seattle office", "priority": "2" }, "sysparm_limit": "21", "sysparm_fields": "number,short_description,description,priority,sys_created_on,assignment_group,location,state", "sysparm_display_value": "false" } } Data Instances

Example dataset record:

{ "user": "Locate employee reports about wireless access point outages in Marketing group for Sao Paulo office region Critical priority incidents for compliance purposes.", "bot": "{ "action": "servicenow.table.search", "parameters": { "tableName": "incident", "query": { "description": "CONTAINS: wireless access point outages", "assignment_group": "Marketing", "location": "Sao Paulo office", "priority": "1" }, "sysparm_limit": "39", "sysparm_fields": "number,short_description,description,priority,sys_created_on,assignment_group,location,state", "sysparm_display_value": "true" }}" } Dataset Creation Source

The conversations were synthetically generated using programmatic generation techniques designed to simulate realistic enterprise IT service desk queries.

Generation introduces variation across:

issue categories

office locations

organizational departments

priority levels

request phrasing

reporting contexts

Example issue types include:

API integration failures

wireless access point outages

network switch port errors

single sign-on errors

firewall configuration errors

power supply failures

password reset requests

email delivery issues

Generation Strategy

Records were generated to maximize variation in:

user intent phrasing

department references

location mentions

priority terminology

reporting context

The output schema enforces consistent structure compatible with ServiceNow incident table queries.

Intended Use

This dataset is intended for:

training LLM agents that interact with enterprise systems

benchmarking tool-calling accuracy

developing AI service desk assistants

research on structured query generation

👤 Creator C.J. Jones AI engineer and developer of domain-specific synthetic datasets for reasoning and diagnostic training in LLMs.

Disclaimer: There are no implied guarrantees and user must accept all risk and resposibilities regarding the use of this and any other datasets provided by CJ Jones.

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