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3a3

Fujitsu Social Digital Twin MCP Server

by 3a3

list_simulations

Retrieve a complete list of all traffic simulations, including IDs, names, statuses, and execution timeframes, to quickly access and manage simulation data.

Instructions

Returns a comprehensive list of all traffic simulations in the system, including their IDs, names, status, and execution timeframes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ctxNo

Implementation Reference

  • The tool definition and handler function for list_simulations. Decorated with @mcp.tool() and registers the tool 'list_simulations' with FastMCP. Calls get_http_client() to create an HTTP client, instantiates FujitsuSocialDigitalTwinClient, and calls api_client.get_simulations() to retrieve the list.
    @mcp.tool()
    async def list_simulations(ctx: Optional[Context] = None) -> Dict[str, Any]:
        """Returns a comprehensive list of all traffic simulations in the system, 
        including their IDs, names, status, and execution timeframes."""
        async with await get_http_client() as client:
            api_client = FujitsuSocialDigitalTwinClient(client)
            result = await api_client.get_simulations()
        return result
  • The @mcp.tool() decorator registers this function as an MCP tool in the FastMCP server instance named 'mcp' (created on line 28).
    @mcp.tool()
  • The get_simulations() method on FujitsuSocialDigitalTwinClient, which performs the actual HTTP GET request to /api/simulations, handles errors, and formats the result via format_simulation_result().
    async def get_simulations(self) -> Dict[str, Any]:
        try:
            response = await self.client.get("/api/simulations")
            response.raise_for_status()
            return format_simulation_result(response.json())
        except httpx.HTTPStatusError as e:
            logger.error(f"Simulation list retrieval error: {e}")
            return format_api_error(e.response.status_code, str(e))
        except Exception as e:
            logger.error(f"Unexpected error retrieving simulations: {e}")
            return format_api_error(500, str(e))
  • The format_simulation_result helper function that wraps the API response in a standard {success: True, data: ...} structure.
    def format_simulation_result(result: Dict[str, Any]) -> Dict[str, Any]:
        return {
            "success": True,
            "data": result
        }
Behavior2/5

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

No annotations are provided, so the description must disclose all behavioral traits. It only describes the return content and lacks details on pagination, ordering, rate limits, or whether it returns active/inactive simulations. This is insufficient for an agent to understand its full behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded with the main action and return fields. It is concise, but could be slightly more informative without being wordy. No wasted space.

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 no output schema and a single parameter (ctx), the description lacks completeness. It does not mention if results are paginated, sorted, or how to handle large lists. The agent would need to infer behavior, which is risky.

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 input schema has one parameter (ctx) with no description at the property level (schema description coverage is 0%). The tool description does not explain the parameter's purpose or usage, though the schema's nested definition provides context. The description adds no value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns a comprehensive list of all traffic simulations with specific fields (IDs, names, status, execution timeframes). This distinguishes it from sibling tools like 'list_simdata' which lists simulation data, not simulations.

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 mentions it returns 'all' simulations but gives no guidance on when to use this tool vs alternatives. It does not explain when not to use it, prerequisites, or context. Sibling tools exist for more specific queries.

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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