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Noctua MCP Server

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create_model

Create a new GO-CAM model for representing biological pathways and processes, providing a foundation for adding genes, functions, and causal relationships in the Noctua MCP Server.

Instructions

Create a new empty GO-CAM model.

Args: title: Optional title for the model

Returns: Barista API response containing the new model ID and editor URLs

Examples:

  • create_model("RAS-RAF signaling pathway")

Notes: - The returned model_id can be used with other tools like add_individual - Models are created in "development" state by default - To add taxon information, use add_individual after creating the model

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function decorated with @mcp.tool() that implements the create_model tool logic. It uses BaristaClient to create a new GO-CAM model, handles responses, validation errors, and constructs editor URLs.
    @mcp.tool()
    async def create_model(
        title: Optional[str] = None
    ) -> Dict[str, Any]:
        """
        Create a new empty GO-CAM model.
    
        Args:
            title: Optional title for the model
    
        Returns:
            Barista API response containing the new model ID and editor URLs
    
        Examples:
    
        - `create_model("RAS-RAF signaling pathway")`
          
    
        Notes:
            - The returned model_id can be used with other tools like add_individual
            - Models are created in "development" state by default
            - To add taxon information, use add_individual after creating the model
        """
        client = get_client()
        resp = client.create_model(title=title)
    
        if resp.validation_failed:
            return {
                "success": False,
                "error": "Validation failed",
                "reason": resp.validation_reason,
                "rolled_back": True
            }
    
        if resp.error:
            return {
                "success": False,
                "error": "Operation failed",
                "reason": resp.error
            }
    
        # Build minimal response
        result = {
            "success": True,
            "model_id": resp.model_id,
            "created": True
        }
    
        # Add editor URLs if we have a model ID
        if resp.model_id:
            import os
            token = os.environ.get("BARISTA_TOKEN", "")
    
            # Graph editor with token
            result["graph_editor_url"] = f"http://noctua-dev.berkeleybop.org/editor/graph/{resp.model_id}?barista_token={token}"
    
            # Pathway editor without token (URL encoded model ID)
            from urllib.parse import quote
            encoded_id = quote(resp.model_id, safe="")
            result["pathway_editor_url"] = f"http://noctua-dev.berkeleybop.org/workbench/noctua-visual-pathway-editor/?model_id={encoded_id}"
    
        return result
Behavior4/5

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 effectively describes key traits: the tool creates a model in 'development' state by default, returns a model_id for use with other tools, and specifies the response format (Barista API response with new model ID and editor URLs). However, it lacks details on permissions, rate limits, or error handling, which could be useful for a creation tool.

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, followed by organized sections (Args, Returns, Examples, Notes). Every sentence adds value without redundancy, such as explaining the return value's utility and default behaviors, 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.

Completeness5/5

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

Given the tool's complexity (creation operation with 1 parameter), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage, parameters, returns, and integration with other tools, providing sufficient context for an agent to invoke it correctly without over-explaining.

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 input schema has 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by explaining that 'title' is optional and provides an example usage, clarifying its purpose beyond the schema. However, it doesn't detail constraints like length or format, leaving some gaps in parameter understanding.

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 the specific action ('Create a new empty GO-CAM model') and distinguishes it from siblings by specifying it creates an empty model, unlike tools like 'add_individual' or 'add_fact' which modify existing models. The verb 'create' is precise and the resource 'GO-CAM model' is well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides when to use this tool (to create a new empty model) and when not to (e.g., for adding taxon information, use 'add_individual' after creation). It also mentions the default state ('development') and references sibling tools like 'add_individual' for subsequent steps, offering clear alternatives and 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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