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JLKmach

ServiceNow MCP Server

by JLKmach

list_articles

Retrieve knowledge articles from ServiceNow with filtering options for knowledge base, category, workflow state, and search queries.

Instructions

List knowledge articles

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of articles to return
offsetNoOffset for pagination
knowledge_baseNoFilter by knowledge base
categoryNoFilter by category
queryNoSearch query for articles
workflow_stateNoFilter by workflow state

Implementation Reference

  • Implements the list_articles tool by querying ServiceNow's kb_knowledge table API with pagination and filters, transforming results into a structured list.
    def list_articles( config: ServerConfig, auth_manager: AuthManager, params: ListArticlesParams, ) -> Dict[str, Any]: """ List knowledge articles with filtering options. Args: config: Server configuration. auth_manager: Authentication manager. params: Parameters for listing articles. Returns: Dictionary with list of articles and metadata. """ api_url = f"{config.api_url}/table/kb_knowledge" # Build query parameters query_params = { "sysparm_limit": params.limit, "sysparm_offset": params.offset, "sysparm_display_value": "all", } # Build query string query_parts = [] if params.knowledge_base: query_parts.append(f"kb_knowledge_base.sys_id={params.knowledge_base}") if params.category: query_parts.append(f"kb_category.sys_id={params.category}") if params.workflow_state: query_parts.append(f"workflow_state={params.workflow_state}") if params.query: query_parts.append(f"short_descriptionLIKE{params.query}^ORtextLIKE{params.query}") if query_parts: query_string = "^".join(query_parts) logger.debug(f"Constructed article query string: {query_string}") query_params["sysparm_query"] = query_string # Log the query parameters for debugging logger.debug(f"Listing articles with query params: {query_params}") # Make request try: response = requests.get( api_url, params=query_params, headers=auth_manager.get_headers(), timeout=config.timeout, ) response.raise_for_status() # Get the JSON response json_response = response.json() logger.debug(f"Article listing raw response: {json_response}") # Safely extract the result if isinstance(json_response, dict) and "result" in json_response: result = json_response.get("result", []) else: logger.error("Unexpected response format: %s", json_response) return { "success": False, "message": f"Unexpected response format", "articles": [], "count": 0, "limit": params.limit, "offset": params.offset, } # Transform the results articles = [] # Handle either string or list if isinstance(result, list): for article_item in result: if not isinstance(article_item, dict): logger.warning("Skipping non-dictionary article item: %s", article_item) continue # Safely extract values article_id = article_item.get("sys_id", "") title = article_item.get("short_description", "") # Extract nested values safely knowledge_base = "" if isinstance(article_item.get("kb_knowledge_base"), dict): knowledge_base = article_item["kb_knowledge_base"].get("display_value", "") category = "" if isinstance(article_item.get("kb_category"), dict): category = article_item["kb_category"].get("display_value", "") workflow_state = "" if isinstance(article_item.get("workflow_state"), dict): workflow_state = article_item["workflow_state"].get("display_value", "") created = article_item.get("sys_created_on", "") updated = article_item.get("sys_updated_on", "") articles.append({ "id": article_id, "title": title, "knowledge_base": knowledge_base, "category": category, "workflow_state": workflow_state, "created": created, "updated": updated, }) else: logger.warning("Result is not a list: %s", result) return { "success": True, "message": f"Found {len(articles)} articles", "articles": articles, "count": len(articles), "limit": params.limit, "offset": params.offset, } except requests.RequestException as e: logger.error(f"Failed to list articles: {e}") return { "success": False, "message": f"Failed to list articles: {str(e)}", "articles": [], "count": 0, "limit": params.limit, "offset": params.offset, }
  • Pydantic model defining input parameters for the list_articles tool, including pagination, filters for knowledge base, category, query, and workflow state.
    class ListArticlesParams(BaseModel): """Parameters for listing knowledge articles.""" limit: int = Field(10, description="Maximum number of articles to return") offset: int = Field(0, description="Offset for pagination") knowledge_base: Optional[str] = Field(None, description="Filter by knowledge base") category: Optional[str] = Field(None, description="Filter by category") query: Optional[str] = Field(None, description="Search query for articles") workflow_state: Optional[str] = Field(None, description="Filter by workflow state")
  • Registers the list_articles tool in the central tool_definitions dictionary used by the MCP server, mapping name to handler function, input schema, return type, description, and serialization method.
    "list_articles": ( list_articles_tool, ListArticlesParams, Dict[str, Any], # Expects dict "List knowledge articles", "raw_dict", # Tool returns raw dict ),
  • Imports the list_articles function from knowledge_base.py with alias list_articles_tool for use in tool registration.
    list_articles as list_articles_tool, )
  • Exposes the list_articles function in the tools package __init__ for easy import.
    list_articles,

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