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vparlapalli490

ServiceNow MCP Server

list_articles

Retrieve ServiceNow knowledge articles with filtering options for knowledge base, category, workflow state, and search queries to find relevant information.

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

  • Handler function that implements the list_articles tool by querying the ServiceNow kb_knowledge table API with filters and transforming the response.
    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 and filtering options.
    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")
  • Registration of the list_articles tool in the central tool definitions dictionary used by the MCP server.
    "list_articles": ( list_articles_tool, ListArticlesParams, Dict[str, Any], # Expects dict "List knowledge articles", "raw_dict", # Tool returns raw dict ),
  • Import of the list_articles handler aliased as list_articles_tool for use in tool registration.
    from servicenow_mcp.tools.knowledge_base import ( list_articles as list_articles_tool, )
  • Re-export of list_articles from knowledge_base module in tools package init.
    from servicenow_mcp.tools.knowledge_base import ( create_article, create_category, create_knowledge_base, get_article, list_articles, list_knowledge_bases, publish_article, update_article, list_categories, )

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