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vparlapalli490

ServiceNow MCP Server

update_article

Modify existing knowledge articles in ServiceNow by updating content, categories, or metadata to maintain accurate documentation.

Instructions

Update an existing knowledge article

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
article_idYesID of the article to update
titleNoUpdated title of the article
textNoUpdated main body text for the article
short_descriptionNoUpdated short description
categoryNoUpdated category for the article
keywordsNoUpdated keywords for search

Implementation Reference

  • The core handler function that executes the update_article tool. It performs a PATCH request to the ServiceNow kb_knowledge table to update the specified article fields.
    def update_article(
        config: ServerConfig,
        auth_manager: AuthManager,
        params: UpdateArticleParams,
    ) -> ArticleResponse:
        """
        Update an existing knowledge article.
    
        Args:
            config: Server configuration.
            auth_manager: Authentication manager.
            params: Parameters for updating the article.
    
        Returns:
            Response with the updated article details.
        """
        api_url = f"{config.api_url}/table/kb_knowledge/{params.article_id}"
    
        # Build request data
        data = {}
    
        if params.title:
            data["short_description"] = params.title
        if params.text:
            data["text"] = params.text
        if params.short_description:
            data["short_description"] = params.short_description
        if params.category:
            data["kb_category"] = params.category
        if params.keywords:
            data["keywords"] = params.keywords
    
        # Make request
        try:
            response = requests.patch(
                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 updated successfully",
                article_id=params.article_id,
                article_title=result.get("short_description"),
                workflow_state=result.get("workflow_state"),
            )
    
        except requests.RequestException as e:
            logger.error(f"Failed to update article: {e}")
            return ArticleResponse(
                success=False,
                message=f"Failed to update article: {str(e)}",
            )
  • Pydantic BaseModel defining the input schema/parameters for the update_article tool, including article_id (required) and optional fields like title, text, etc.
    class UpdateArticleParams(BaseModel):
        """Parameters for updating a knowledge article."""
    
        article_id: str = Field(..., description="ID of the article to update")
        title: Optional[str] = Field(None, description="Updated title of the article")
        text: Optional[str] = Field(None, description="Updated main body text for the article")
        short_description: Optional[str] = Field(None, description="Updated short description")
        category: Optional[str] = Field(None, description="Updated category for the article")
        keywords: Optional[str] = Field(None, description="Updated keywords for search")
  • Tool registration in the central tool_definitions dictionary used by the MCP server. Maps 'update_article' to its handler function (aliased as update_article_tool), input schema (UpdateArticleParams), return type hint, description, and serialization method.
    "update_article": (
        update_article_tool,
        UpdateArticleParams,
        str,  # Expects JSON string
        "Update an existing knowledge article",
        "json_dict",  # Tool returns Pydantic model
  • Import of the update_article handler function from knowledge_base.py, aliased as update_article_tool for use in tool registration.
    from servicenow_mcp.tools.knowledge_base import (
        update_article as update_article_tool,
    )
  • Re-export of update_article from knowledge_base.py in the tools package __init__.py, making it available for imports.
    update_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. While 'update' implies mutation, it doesn't describe permissions required, whether updates are reversible, what happens to unspecified fields (partial vs. full updates), error conditions, or response format. This is inadequate for a mutation tool with zero annotation coverage.

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 appropriately sized for a basic tool description and front-loads the essential information (update + resource).

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 6 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain behavioral aspects, usage context, or what the tool returns. The 100% schema coverage helps with parameters, but other critical context is missing for proper tool selection and invocation.

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 already documents all 6 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, 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 'Update an existing knowledge article' clearly states the verb (update) and resource (knowledge article), but it's generic and doesn't differentiate from sibling tools like update_catalog_category, update_change_request, etc. It specifies the resource type (knowledge article) which helps somewhat, but lacks specificity about what aspects can be updated.

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. It doesn't mention prerequisites (e.g., article must exist), doesn't distinguish from create_article or publish_article, and offers no context about appropriate scenarios for updating versus other operations on knowledge articles.

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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