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read_project_collaborators

Retrieve a list of all collaborators working on a specific QuantConnect project to manage team access and permissions.

Instructions

List all collaborators on a project.

Args: project_id: ID of the project to list collaborators for

Returns: Dictionary containing list of project collaborators

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The core handler function for the 'read_project_collaborators' tool. It authenticates with QuantConnect, sends a POST request to the 'projects/collaboration/read' endpoint with the project ID, parses the response, and returns the list of collaborators or an error message.
    @mcp.tool()
    async def read_project_collaborators(project_id: int) -> Dict[str, Any]:
        """
        List all collaborators on a project.
    
        Args:
            project_id: ID of the project to list collaborators for
    
        Returns:
            Dictionary containing list of project collaborators
        """
        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="projects/collaboration/read", method="POST", json=request_data
            )
    
            # Parse response
            if response.status_code == 200:
                data = response.json()
    
                if data.get("success", False):
                    collaborators = data.get("collaborators", [])
                    
                    return {
                        "status": "success",
                        "project_id": project_id,
                        "collaborators": collaborators,
                        "total_collaborators": len(collaborators),
                        "message": f"Successfully retrieved {len(collaborators)} collaborators for project {project_id}",
                    }
                else:
                    # API returned success=false
                    errors = data.get("errors", ["Unknown error"])
                    return {
                        "status": "error",
                        "error": "Failed to read project collaborators",
                        "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 read project collaborators: {str(e)}",
                "project_id": project_id,
            }
  • Registration of project tools module (including read_project_collaborators) by calling register_project_tools(mcp) in the main entry point.
    register_auth_tools(mcp)
    register_project_tools(mcp)
    register_file_tools(mcp)
    register_backtest_tools(mcp)
    register_live_tools(mcp)
    register_optimization_tools(mcp)
  • Alternative registration of project tools module in server.py main function.
    register_auth_tools(mcp)
    register_project_tools(mcp)
    register_file_tools(mcp)
    register_backtest_tools(mcp)
    register_live_tools(mcp)
    register_optimization_tools(mcp)
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. It states this is a read operation ('List'), implying it's non-destructive, but doesn't disclose behavioral traits like authentication needs, rate limits, pagination, or error conditions. For a tool with no annotation coverage, this is a significant gap.

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 well-structured and front-loaded with the core purpose in the first sentence, followed by clear sections for Args and Returns. Every sentence earns its place without redundancy, making it efficient and easy to parse.

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 low complexity (single parameter, read-only operation) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete behavioral disclosure, it lacks depth for safe and effective use.

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

Parameters4/5

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

The description adds meaningful context for the single parameter 'project_id' by explaining it's 'ID of the project to list collaborators for', which clarifies its role beyond the schema's basic type (integer). With 0% schema description coverage and only one parameter, this adequately compensates, though it could specify format constraints.

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 ('collaborators on a project'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'read_project' or 'read_project_nodes', which also read project-related data, so it doesn't achieve full 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?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'read_project' (which might include collaborators) and 'create_project_collaborator'/'delete_project_collaborator' (which manage collaborators), the description lacks context on usage scenarios or exclusions.

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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