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get_dashboard_data

Retrieve dashboard data for specific chart types to visualize threat modeling metrics, supporting pagination, date ranges, and project filtering.

Instructions

Get dashboard data for a specific chart type

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chart_typeYes
limitNo
pageNo
startNo
endNo
project_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler function for 'get_dashboard_data'. Registered via @mcp.tool() decorator. Fetches dashboard data by calling the API client.
    @mcp.tool()
    async def get_dashboard_data(chart_type: str, limit: int = 20, page: int = 0, start: str = None, end: str = None, project_id: str = None) -> str:
        """Get dashboard data for a specific chart type"""
        async with create_client_from_env() as client:
            result = await client.get_dashboard_data(
                chart_type=chart_type,
                limit=limit,
                page=page,
                start=start,
                end=end,
                project_id=project_id
            )
            return str(result)
  • Helper method in DeviciAPIClient that constructs API parameters and makes authenticated GET request to /dashboard/ endpoint to retrieve the data.
    async def get_dashboard_data(
        self, 
        chart_type: str, 
        limit: int = 20, 
        page: int = 0,
        start: Optional[str] = None,
        end: Optional[str] = None,
        project_id: Optional[str] = None
    ) -> List[Dict[str, Any]]:
        """Get dashboard data by specific chart type."""
        params = {
            "limit": limit,
            "page": page,
            "type": chart_type
        }
        if start:
            params["start"] = start
        if end:
            params["end"] = end
        if project_id:
            params["projectId"] = project_id
            
        return await self._make_request("GET", "/dashboard/", params=params)
  • The @mcp.tool() decorator registers the get_dashboard_data function as an MCP tool.
    @mcp.tool()
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. It states 'Get dashboard data' which implies a read-only operation, but doesn't cover critical aspects like authentication needs, rate limits, error handling, or what the output contains. For a tool with 6 parameters and no annotation coverage, this leaves significant gaps in understanding its behavior.

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 is front-loaded with the core purpose. There's no wasted wording, and it directly states what the tool does without unnecessary elaboration, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 6 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is incomplete. It covers the basic purpose but lacks details on parameters, usage context, and behavioral traits. The output schema mitigates some gaps by documenting return values, but overall, the description doesn't provide enough context for effective tool invocation.

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

Parameters2/5

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

Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description only mentions 'chart_type' implicitly, but doesn't explain any of the 6 parameters (e.g., what 'limit', 'page', 'start', 'end', 'project_id' do or their formats). It adds minimal value beyond the schema, failing to compensate for the coverage gap.

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 states the tool 'Get dashboard data for a specific chart type' which provides a clear verb ('Get') and resource ('dashboard data'), but it's vague about what 'dashboard data' entails and doesn't distinguish from siblings like 'get_dashboard_types'. It specifies 'for a specific chart type' which adds some specificity, but overall lacks detailed differentiation from related tools.

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 explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for retrieving dashboard data based on chart type, but it doesn't mention prerequisites, exclusions, or compare to siblings like 'get_dashboard_types'. Without this, the agent has minimal context for tool selection.

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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