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javerthl

ServiceNow MCP Server

by javerthl

get_article

Retrieve specific knowledge articles from ServiceNow using their unique ID to access documented solutions and information.

Instructions

Get a specific knowledge article by ID

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_idYesID of the article to get

Implementation Reference

  • The handler function that implements the get_article tool logic. It makes a GET request to the ServiceNow API to fetch the article by ID, extracts and formats the response data.
    def get_article(
        config: ServerConfig,
        auth_manager: AuthManager,
        params: GetArticleParams,
    ) -> Dict[str, Any]:
        """
        Get a specific knowledge article by ID.
    
        Args:
            config: Server configuration.
            auth_manager: Authentication manager.
            params: Parameters for getting the article.
    
        Returns:
            Dictionary with article details.
        """
        api_url = f"{config.api_url}/table/kb_knowledge/{params.article_id}"
    
        # Build query parameters
        query_params = {
            "sysparm_display_value": "true",
        }
    
        # 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()
            
            # 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": "Unexpected response format",
                }
    
            if not result or not isinstance(result, dict):
                return {
                    "success": False,
                    "message": f"Article with ID {params.article_id} not found",
                }
    
            # Extract values safely
            article_id = result.get("sys_id", "")
            title = result.get("short_description", "")
            text = result.get("text", "")
            
            # Extract nested values safely
            knowledge_base = ""
            if isinstance(result.get("kb_knowledge_base"), dict):
                knowledge_base = result["kb_knowledge_base"].get("display_value", "")
            
            category = ""
            if isinstance(result.get("kb_category"), dict):
                category = result["kb_category"].get("display_value", "")
            
            workflow_state = ""
            if isinstance(result.get("workflow_state"), dict):
                workflow_state = result["workflow_state"].get("display_value", "")
            
            author = ""
            if isinstance(result.get("author"), dict):
                author = result["author"].get("display_value", "")
            
            keywords = result.get("keywords", "")
            article_type = result.get("article_type", "")
            views = result.get("view_count", "0")
            created = result.get("sys_created_on", "")
            updated = result.get("sys_updated_on", "")
    
            article = {
                "id": article_id,
                "title": title,
                "text": text,
                "knowledge_base": knowledge_base,
                "category": category,
                "workflow_state": workflow_state,
                "created": created,
                "updated": updated,
                "author": author,
                "keywords": keywords,
                "article_type": article_type,
                "views": views,
            }
    
            return {
                "success": True,
                "message": "Article retrieved successfully",
                "article": article,
            }
    
        except requests.RequestException as e:
            logger.error(f"Failed to get article: {e}")
            return {
                "success": False,
                "message": f"Failed to get article: {str(e)}",
            }
  • Pydantic BaseModel defining the input parameters for the get_article tool, specifically requiring the article_id.
    class GetArticleParams(BaseModel):
        """Parameters for getting a knowledge article."""
    
        article_id: str = Field(..., description="ID of the article to get")
  • Registration of the get_article tool in the central tool definitions dictionary, linking the handler function, input schema, return type hint, description, and serialization method.
    "get_article": (
        get_article_tool,
        GetArticleParams,
        Dict[str, Any],  # Expects dict
        "Get a specific knowledge article by ID",
        "raw_dict",  # Tool returns raw dict
  • Import of the get_article function in the tools package __init__.py, making it available for import.
    get_article,
  • Inclusion of get_article in the __all__ list for the tools package, exposing it publicly.
    "get_article",
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool retrieves an article but doesn't mention whether this is a read-only operation, what permissions are required, what happens if the ID doesn't exist, or any rate limits. For a retrieval tool with zero annotation coverage, 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 a single, focused sentence that efficiently communicates the core functionality without unnecessary words. It's front-loaded with the essential information and contains zero redundant or verbose elements. This is an excellent example of conciseness.

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

Completeness3/5

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

For a simple retrieval tool with one well-documented parameter and no output schema, the description provides the minimum viable information about what the tool does. However, it lacks context about error handling, return format, or how it differs from sibling tools. Without annotations or output schema, the description should ideally provide more behavioral context to be fully complete.

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?

The schema description coverage is 100%, with the single parameter 'article_id' clearly documented in the schema. The description adds no additional parameter information beyond what's in the schema (e.g., format examples, ID sources, or validation rules). This meets the baseline score of 3 when schema coverage is high.

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

Purpose4/5

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

The description clearly states the action ('Get') and resource ('a specific knowledge article by ID'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'list_articles' or 'create_article', but the specificity of 'by ID' provides some implicit distinction. The description avoids tautology by not simply repeating the tool name.

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 like 'list_articles' for browsing or 'create_article' for creation. There's no mention of prerequisites, error conditions, or typical use cases. The agent must infer usage from the name and description alone without explicit direction.

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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