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michaelneale

Goose App Maker MCP

by michaelneale

app_list

View available web applications created with Goose App Maker MCP to manage and serve apps from configurable directories.

Instructions

List all available web applications.

Returns:
    A dictionary containing the list of available apps and their details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:117-162 (handler)
    Handler function for the 'app_list' tool. Lists directories in APP_DIR (~/.config/goose/app-maker-apps), collects file lists and manifest info for each app.
    @mcp.tool()
    def app_list() -> Dict[str, Any]:
        """
        List all available web applications.
        
        Returns:
            A dictionary containing the list of available apps and their details
        """
        try:
            apps = []
            for app_dir in Path(APP_DIR).iterdir():
                if app_dir.is_dir():
                    app_info = {
                        "name": app_dir.name,
                        "path": str(app_dir),
                        "files": []
                    }
                    
                    # Get the list of files
                    for file_path in app_dir.glob("**/*"):
                        if file_path.is_file():
                            rel_path = str(file_path.relative_to(app_dir))
                            app_info["files"].append(rel_path)
                    
                    # Check if there's a goose-app-manifest.json file
                    manifest_path = app_dir / "goose-app-manifest.json"
                    if manifest_path.exists():
                        try:
                            with open(manifest_path, 'r') as f:
                                manifest = json.load(f)
                                app_info["manifest"] = manifest
                        except json.JSONDecodeError:
                            app_info["manifest_error"] = "Invalid goose-app-manifest.json file"
                    
                    apps.append(app_info)
            
            return {
                "success": True,
                "apps": apps,
                "count": len(apps),
                "app_dir": APP_DIR
            }
        except Exception as e:
            logger.error(f"Error listing apps: {e}")
            return {"success": False, "error": f"Failed to list apps: {str(e)}"}
  • main.py:117-117 (registration)
    Registration of the app_list tool using the @mcp.tool() decorator.
    @mcp.tool()
Behavior3/5

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

No annotations are provided, so the description carries full burden. It mentions the return format ('dictionary containing the list of available apps and their details'), which adds useful context beyond basic purpose. However, it doesn't disclose behavioral traits like whether this is a read-only operation, potential rate limits, or authentication requirements, leaving gaps in transparency.

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 with two sentences: one stating the purpose and one describing the return value. It's front-loaded with the core functionality and avoids unnecessary details, though it could be slightly more structured by explicitly labeling sections.

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

Completeness4/5

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

Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is reasonably complete. It covers purpose and return format, and the output schema handles return values, so no major gaps exist. However, it lacks behavioral context like safety or performance considerations, preventing a perfect score.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to add parameter semantics, and it appropriately avoids discussing parameters, earning a baseline score of 4 for this context.

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 tool's purpose with a specific verb ('List') and resource ('web applications'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'app_refresh' or 'app_serve' which might also involve listing operations, so it doesn't reach the highest score.

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 provides no guidance on when to use this tool versus alternatives. With siblings like 'app_refresh' and 'app_serve' that might overlap in functionality, there's no explicit or implied context for choosing this specific listing tool, leaving usage unclear.

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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