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JLKmach

ServiceNow MCP Server

by JLKmach

list_articles

Retrieve knowledge articles from ServiceNow with filters for knowledge base, category, workflow state, or 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

  • Implementation of the list_articles tool handler. Queries the ServiceNow kb_knowledge table via REST API with filters for knowledge_base, category, workflow_state, and query. Processes and transforms the response into a structured list of articles with metadata.
    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 BaseModel defining the input parameters for the list_articles tool, including pagination (limit, offset) and filters (knowledge_base, category, query, 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")
  • Registration entry for the 'list_articles' tool in the central tool_definitions dictionary. Maps the tool name to its handler function (list_articles_tool), input schema (ListArticlesParams), return type hint, description, and serialization method ('raw_dict').
    "list_articles": (
        list_articles_tool,
        ListArticlesParams,
        Dict[str, Any],  # Expects dict
        "List knowledge articles",
        "raw_dict",  # Tool returns raw dict
    ),
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. 'List knowledge articles' implies a read-only operation but doesn't disclose pagination behavior (beyond what's in the schema), rate limits, authentication requirements, or what happens with filters. For a tool with 6 parameters and no annotations, this leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise at just three words. While this may be too brief for completeness, it's perfectly front-loaded with zero wasted words. Every word directly contributes to the core purpose statement.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what knowledge articles are, how results are returned, or provide any context about the listing operation. For a tool with moderate complexity and no structured support, this leaves too many gaps for effective use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 6 parameters (limit, offset, knowledge_base, category, query, workflow_state). The description adds no parameter information beyond what's in the schema. According to guidelines, when schema coverage is high (>80%), the baseline is 3 even with no param info in description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'List knowledge articles' states the basic verb+resource but lacks specificity. It doesn't distinguish this from other list tools (like list_knowledge_bases or list_categories) or explain what 'knowledge articles' are in this context. The purpose is clear at a high level but vague about scope and differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools (including get_article for single articles and other list_* tools), there's no mention of when this is appropriate versus other listing or retrieval methods. No prerequisites, exclusions, or context are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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