Skip to main content
Glama
javerthl

ServiceNow MCP Server

by javerthl

create_article

Create knowledge articles in ServiceNow by providing title, content, category, and knowledge base details to document solutions and procedures.

Instructions

Create a new knowledge article

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_typeNoThe type of article. Options are 'text' or 'wiki'. text lets the text field support html formatting. wiki lets the text field support wiki markup.html
categoryYesCategory for the article
keywordsNoKeywords for search
knowledge_baseYesThe knowledge base to create the article in
short_descriptionYesShort description of the article
textYesThe main body text for the article. Field supports html formatting and wiki markup based on the article_type. HTML is the default.
titleYesTitle of the article

Implementation Reference

  • The handler function that implements the core logic for creating a knowledge article by making a POST request to the ServiceNow 'kb_knowledge' table API endpoint.
    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 parameters and validation schema 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.")
  • Tool registration in the central tool_definitions dictionary, associating the tool name 'create_article' with its handler function, input schema, description, and serialization details.
    "create_article": (
        create_article_tool,
        CreateArticleParams,
        str,  # Expects JSON string
        "Create a new knowledge article",
        "json_dict",  # Tool returns Pydantic model
    ),
  • Exposes the create_article function in the tools package __all__ list for easy import.
    "create_article",
  • Import alias for the create_article handler used in tool registration.
    create_article as create_article_tool,
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 only states the basic action ('create'). It lacks details on permissions required, whether creation is idempotent, what happens on failure, or the format of the response. For a mutation 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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose and appropriately sized for the tool's complexity.

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 (e.g., returns an ID, triggers workflows), error conditions, or how it relates to other knowledge management tools like 'publish_article'.

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 each parameter well-documented in the schema itself (e.g., article_type options explained, text formatting details). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline for high schema coverage.

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_category' or 'create_knowledge_base', which also create resources in the same domain, leaving some ambiguity about scope.

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?

No guidance is provided on when to use this tool versus alternatives like 'update_article' or 'publish_article', nor does it mention prerequisites such as needing an existing knowledge base. The description assumes context without explicit usage instructions.

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/javerthl/servicenow-mcp'

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