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ServiceNow MCP Server

create_article

Generate knowledge articles by specifying title, text, category, and knowledge base for ServiceNow, enabling structured content creation and management.

Instructions

Create a new knowledge article

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Implementation Reference

  • The handler function that implements the create_article tool by making a POST request to ServiceNow's kb_knowledge table with the provided parameters.
    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 (parameters) for the create_article tool.
    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. Field supports html formatting and wiki markup based on the article_type. HTML is the default.")
        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("html", description="The type of article. Options are 'text' or 'wiki'. text lets the text field support html formatting. wiki lets the text field support wiki markup.")
  • Registration of the create_article tool in the central tool definitions dictionary used for MCP server.
    "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 aliased as create_article_tool for use in tool registration.
        create_article as create_article_tool,
    )
  • Re-export of create_article from knowledge_base module in tools package __init__.
    from servicenow_mcp.tools.knowledge_base import (
        create_article,
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Create a new knowledge article' implies a write/mutation operation, but it doesn't disclose any behavioral traits like permission requirements, whether the article is immediately published or in draft state, what happens on duplicate titles, or any rate limits. For a creation tool with zero annotation coverage, this is insufficient.

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 - a single 5-word sentence that gets straight to the point with zero wasted words. It's perfectly front-loaded and appropriately sized for what it does convey.

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 creation tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what parameters are needed, what the tool returns, or any behavioral context. While conciseness is good, the description fails to provide the necessary context for effective tool use.

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

Parameters1/5

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

The description mentions no parameters at all, while the input schema shows 7 parameters (article_type, category, keywords, knowledge_base, short_description, text, title) with 5 required. With 0% schema description coverage and no parameter information in the description, this represents a significant gap in documentation.

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 'Create a new knowledge article' clearly states the verb (create) and resource (knowledge article), but it's somewhat generic and doesn't differentiate from sibling tools like 'create_knowledge_base' or 'create_category'. It's adequate but lacks specificity about what distinguishes this particular creation operation.

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. There are multiple 'create' tools in the sibling list (create_knowledge_base, create_category, create_change_request, etc.), but no indication of when this specific article creation tool is appropriate versus those other creation operations.

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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