Update README.md

README.md

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----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-sa-4.0
+language:
+- en
+pretty_name: ServiceNow Incident Search Conversations
+size_categories:
+- n<1K
+task_categories:
+- information-extraction
+- question-answering
+task_ids:
+- text-to-structured
+- tool-use
+- function-calling
+tags:
+- llm
+- tool-calling
+- function-calling
+- servicenow
+- it-service-management
+- itsm
+- enterprise-ai
+- support-automation
+- incident-management
+- structured-output
+- api-generation
+- ai-assistant
+- enterprise-automation
+- rag-compatible
+- synthetic-data
+- prompt-engineering
+- dataset-generation
+- conversational-ai
+- devops
+- helpdesk
+annotations_creators:
+- machine-generated
+language_creators:
+- machine-generated
+multilinguality:
+- monolingual
+source_datasets:
+- synthetic
+domain:
+- enterprise
+- information-technology
+- customer-support
+- devops
+task_domain:
+- enterprise-ai
+- workflow-automation
+author:
+- C. J. Jones
+dataset_type:
+- conversational
+- structured-query
+version: 1.0.0
+---
+This dataset contains structured User → Bot conversations demonstrating how a natural language request can be translated into a structured ServiceNow incident search API call.
+
+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