Skip to main content
Glama
vparlapalli490

ServiceNow MCP Server

create_article

Create knowledge articles in ServiceNow by providing title, content, description, category, and target knowledge base for documentation and information sharing.

Instructions

Create a new knowledge article

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesTitle of the article
textYesThe main body text for the article
short_descriptionYesShort description of the article
knowledge_baseYesThe knowledge base to create the article in
categoryYesCategory for the article
keywordsNoKeywords for search
article_typeNoThe type of articletext

Implementation Reference

  • The core handler function implementing the create_article tool. It constructs a POST request to the ServiceNow kb_knowledge table API with the provided article details and returns an ArticleResponse.
    def create_article(
        config: ServerConfig,
        auth_manager: AuthManager,
        params: CreateArticleParams,
    ) -> ArticleResponse:
        """
        Create a new knowledge article.
    
        Args:
            config: Server configuration.
            auth_manager: Authentication manager.
            params: Parameters for creating the article.
    
        Returns:
            Response with the created article details.
        """
        api_url = f"{config.api_url}/table/kb_knowledge"
    
        # Build request data
        data = {
            "short_description": params.short_description,
            "text": params.text,
            "kb_knowledge_base": params.knowledge_base,
            "kb_category": params.category,
            "article_type": params.article_type,
        }
    
        if params.title:
            data["short_description"] = params.title
        if params.keywords:
            data["keywords"] = params.keywords
    
        # Make request
        try:
            response = requests.post(
                api_url,
                json=data,
                headers=auth_manager.get_headers(),
                timeout=config.timeout,
            )
            response.raise_for_status()
    
            result = response.json().get("result", {})
    
            return ArticleResponse(
                success=True,
                message="Article created successfully",
                article_id=result.get("sys_id"),
                article_title=result.get("short_description"),
                workflow_state=result.get("workflow_state"),
            )
    
        except requests.RequestException as e:
            logger.error(f"Failed to create article: {e}")
            return ArticleResponse(
                success=False,
                message=f"Failed to create article: {str(e)}",
            )
  • Pydantic BaseModel defining the input schema/validation for the create_article tool parameters.
    class CreateArticleParams(BaseModel):
        """Parameters for creating a knowledge article."""
    
        title: str = Field(..., description="Title of the article")
        text: str = Field(..., description="The main body text for the article")
        short_description: str = Field(..., description="Short description of the article")
        knowledge_base: str = Field(..., description="The knowledge base to create the article in")
        category: str = Field(..., description="Category for the article")
        keywords: Optional[str] = Field(None, description="Keywords for search")
        article_type: Optional[str] = Field("text", description="The type of article")
  • Tool registration in the get_tool_definitions() dictionary, associating 'create_article' name with its handler function (aliased), input schema, return type hint, description, and serialization method.
    "create_article": (
        create_article_tool,
        CreateArticleParams,
        str,  # Expects JSON string
        "Create a new knowledge article",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Import of the create_article handler function aliased as create_article_tool for use in tool registration.
    create_article as create_article_tool,
  • Re-export of create_article from knowledge_base.py for convenient access in the tools package.
    create_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 but offers minimal information. 'Create' implies a write operation, but it doesn't address permissions needed, whether the article is draft/published by default, what happens on duplicate titles, or what the response contains. This leaves significant gaps for a mutation tool.

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, efficient sentence that states the core purpose without unnecessary words. It's appropriately sized for a straightforward creation tool and gets directly to the point with zero wasted verbiage.

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?

For a mutation tool with 7 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what happens after creation, whether there are side effects, what permissions are required, or how this interacts with related operations like publishing. The context demands more comprehensive guidance.

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 has 100% description coverage, providing clear documentation for all 7 parameters. The description adds no additional parameter information beyond what's already in the schema, so it meets the baseline of 3 but doesn't enhance understanding of parameter relationships or usage context.

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 'Create a new knowledge article' clearly states the action (create) and resource (knowledge article), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'create_knowledge_base' or 'create_category' that also create resources in the same domain, preventing a perfect score.

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 sibling tools like 'update_article' and 'publish_article' available, there's no indication of prerequisites, sequencing, or when this creation tool should be selected over other article-related operations.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/vparlapalli490/MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server