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stop_live_algorithm

Stop a live trading algorithm by providing the project ID to halt execution and manage active strategies.

Instructions

Stop a live algorithm.

Args: project_id: ID of the project with the live algorithm to stop

Returns: Dictionary containing stop result

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for the 'stop_live_algorithm' tool. It authenticates with QuantConnect, prepares a request with the project ID, and sends a POST request to the 'live/update/stop' API endpoint to stop the live algorithm. Handles various response cases including success, errors, and exceptions.
    @mcp.tool()
    async def stop_live_algorithm(project_id: int) -> Dict[str, Any]:
        """
        Stop a live algorithm.
    
        Args:
            project_id: ID of the project with the live algorithm to stop
    
        Returns:
            Dictionary containing stop result
        """
        auth = get_auth_instance()
        if auth is None:
            return {
                "status": "error",
                "error": "QuantConnect authentication not configured. Use configure_auth() first.",
            }
    
        try:
            # Prepare request data
            request_data = {"projectId": project_id}
    
            # Make API request
            response = await auth.make_authenticated_request(
                endpoint="live/update/stop", method="POST", json=request_data
            )
    
            # Parse response
            if response.status_code == 200:
                data = response.json()
    
                if data.get("success", False):
                    return {
                        "status": "success",
                        "project_id": project_id,
                        "message": f"Successfully stopped live algorithm for project {project_id}",
                    }
                else:
                    # API returned success=false
                    errors = data.get("errors", ["Unknown error"])
                    return {
                        "status": "error",
                        "error": "Live algorithm stop failed",
                        "details": errors,
                        "project_id": project_id,
                    }
    
            elif response.status_code == 401:
                return {
                    "status": "error",
                    "error": "Authentication failed. Check your credentials and ensure they haven't expired.",
                }
    
            else:
                return {
                    "status": "error",
                    "error": f"API request failed with status {response.status_code}",
                    "response_text": (
                        response.text[:500]
                        if hasattr(response, "text")
                        else "No response text"
                    ),
                }
    
        except Exception as e:
            return {
                "status": "error",
                "error": f"Failed to stop live algorithm: {str(e)}",
                "project_id": project_id,
            }
  • Calls register_live_tools(mcp) to register all live trading tools, including 'stop_live_algorithm', with the MCP server instance.
    register_live_tools(mcp)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool stops a live algorithm, implying a mutation operation, but lacks details on permissions required, whether the stop is reversible, side effects (e.g., on trading positions), rate limits, or error conditions. 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.

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first ('Stop a live algorithm.'), followed by brief parameter and return sections. There's no wasted text, though the structure could be slightly improved by integrating parameter details more seamlessly.

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's complexity (a mutation operation with no annotations), the description is minimally complete. It covers the basic purpose and parameter, and an output schema exists, so return values needn't be explained. However, for a tool that stops live algorithms, more context on behavior and usage is warranted to compensate for the lack of annotations.

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 0%, so the description must compensate, but it only minimally adds meaning. It explains that 'project_id' is the 'ID of the project with the live algorithm to stop', which clarifies the parameter's role beyond the schema's title ('Project Id') and type. However, it doesn't detail format constraints (e.g., integer range) or provide examples, leaving gaps in understanding.

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 action ('Stop') and target resource ('a live algorithm'), which is specific and unambiguous. However, it doesn't differentiate from sibling tools like 'liquidate_live_algorithm' or 'abort_optimization', which might have overlapping purposes in stopping algorithmic processes.

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. The description doesn't mention prerequisites (e.g., whether the algorithm must be running), exclusions, or comparisons to siblings like 'liquidate_live_algorithm', leaving the agent to infer usage context.

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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