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JLKmach

ServiceNow MCP Server

by JLKmach

list_projects

Retrieve and filter ServiceNow projects by state, assignment group, or timeframe to manage project visibility and tracking.

Instructions

List projects from ServiceNow

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of records to return
offsetNoOffset to start from
stateNoFilter by state
assignment_groupNoFilter by assignment group
timeframeNoFilter by timeframe (upcoming, in-progress, completed)
queryNoAdditional query string

Implementation Reference

  • The handler function that implements the list_projects tool. It validates parameters using ListProjectsParams, builds a query for filtering projects, makes a GET request to the ServiceNow pm_project table API, and returns the list of projects.
    def list_projects(
        config: ServerConfig,  # Changed from auth_manager
        auth_manager: AuthManager,  # Changed from server_config
        params: Dict[str, Any],
    ) -> Dict[str, Any]:
        """
        List projects from ServiceNow.
    
        Args:
            config: The server configuration.
            auth_manager: The authentication manager.
            params: The parameters for listing projects.
    
        Returns:
            A list of projects.
        """
        # Unwrap and validate parameters
        result = _unwrap_and_validate_params(
            params, 
            ListProjectsParams
        )
        
        if not result["success"]:
            return result
        
        validated_params = result["params"]
        
        # Build the query
        query_parts = []
        
        if validated_params.state:
            query_parts.append(f"state={validated_params.state}")
        if validated_params.assignment_group:
            query_parts.append(f"assignment_group={validated_params.assignment_group}")
        
        # Handle timeframe filtering
        if validated_params.timeframe:
            now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
            if validated_params.timeframe == "upcoming":
                query_parts.append(f"start_date>{now}")
            elif validated_params.timeframe == "in-progress":
                query_parts.append(f"start_date<{now}^end_date>{now}")
            elif validated_params.timeframe == "completed":
                query_parts.append(f"end_date<{now}")
        
        # Add any additional query string
        if validated_params.query:
            query_parts.append(validated_params.query)
        
        # Combine query parts
        query = "^".join(query_parts) if query_parts else ""
        
        # Get the instance URL
        instance_url = _get_instance_url(auth_manager, config)
        if not instance_url:
            return {
                "success": False,
                "message": "Cannot find instance_url in either server_config or auth_manager",
            }
        
        # Get the headers
        headers = _get_headers(auth_manager, config)
        if not headers:
            return {
                "success": False,
                "message": "Cannot find get_headers method in either auth_manager or server_config",
            }
        
        # Make the API request
        url = f"{instance_url}/api/now/table/pm_project"
        
        params = {
            "sysparm_limit": validated_params.limit,
            "sysparm_offset": validated_params.offset,
            "sysparm_query": query,
            "sysparm_display_value": "true",
        }
        
        try:
            response = requests.get(url, headers=headers, params=params)
            response.raise_for_status()
            
            result = response.json()
            
            # Handle the case where result["result"] is a list
            projects = result.get("result", [])
            count = len(projects)
            
            return {
                "success": True,
                "projects": projects,
                "count": count,
                "total": count,  # Use count as total if total is not provided
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Error listing projects: {e}")
            return {
                "success": False,
                "message": f"Error listing projects: {str(e)}",
            }
  • Pydantic model defining the input parameters for the list_projects tool, including pagination, filters, and custom query options.
    class ListProjectsParams(BaseModel):
        """Parameters for listing projects."""
    
        limit: Optional[int] = Field(10, description="Maximum number of records to return")
        offset: Optional[int] = Field(0, description="Offset to start from")
        state: Optional[str] = Field(None, description="Filter by state")
        assignment_group: Optional[str] = Field(None, description="Filter by assignment group")
        timeframe: Optional[str] = Field(None, description="Filter by timeframe (upcoming, in-progress, completed)")
        query: Optional[str] = Field(None, description="Additional query string")
  • Registration of the list_projects tool in the central tool definitions dictionary used by the MCP server, mapping name to handler, schema, description, etc.
    "list_projects": (
        list_projects_tool,
        ListProjectsParams,
        str,  # Expects JSON string
        "List projects from ServiceNow",
        "json",  # Tool returns list/dict
    ),
  • Import of the list_projects function into the tools package __init__.py for exposure.
    from servicenow_mcp.tools.project_tools import (
        create_project,
        update_project,
        list_projects,
    )
  • Inclusion of list_projects in the __all__ list for public API exposure.
    "list_projects",
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'List projects from ServiceNow' implies a read-only operation, but it doesn't specify whether this requires authentication, what permissions are needed, whether results are paginated, the format of returned data, or any rate limits. For a tool with 6 parameters and no annotation coverage, this is a significant gap in behavioral context.

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 - just 4 words. It's front-loaded with the essential information (list projects) and has zero wasted words. Every word earns its place, making it efficient for an AI agent to parse quickly.

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?

Given the tool has 6 parameters, no annotations, and no output schema, the description is insufficiently complete. While the schema covers parameters well, the description doesn't address behavioral aspects (authentication, permissions, pagination), return format, or usage context. For a list operation with filtering capabilities, more context would help the agent understand what to expect and how to use it effectively.

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 input schema has 100% description coverage, with each parameter clearly documented. The description adds no parameter information beyond what's already in the schema. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description. The description doesn't compensate for any gaps because there are none in the schema.

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 verb ('List') and resource ('projects from ServiceNow'), making the purpose immediately understandable. However, it doesn't differentiate this from other list_* tools on the server (like list_articles, list_change_requests, etc.), which would require a 5. The description is specific but lacks sibling differentiation.

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 many sibling tools available (including create_project and update_project), there's no indication of when listing projects is appropriate versus creating or updating them. The description offers no context about prerequisites, typical use cases, or relationship to other tools.

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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