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gleachkr
by gleachkr

get_project_status

Check the status of an Aristotle project to retrieve full project data including available solutions, using the project ID.

Instructions

Checks the status of a specific Aristotle project. Returns full project data including solution if available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYes
save_solution_toNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:66-102 (handler)
    The handler function for the 'get_project_status' tool. It retrieves the project by ID, constructs status data, downloads the solution if complete (optionally saving it), and returns JSON.
    @mcp.tool()
    async def get_project_status(
        project_id: str,
        save_solution_to: Optional[str] = None,
    ) -> str:
        """
        Checks the status of a specific Aristotle project.
        Returns full project data including solution if available.
        """
        project = await Project.from_id(project_id)
        
        data = {
            "project_id": project.project_id,
            "status": project.status.value,
            "created_at": project.created_at.isoformat(),
            "last_updated_at": project.last_updated_at.isoformat(),
            "file_name": project.file_name,
            "description": project.description,
        }
        
        if project.status == ProjectStatus.COMPLETE:
            try:
                with tempfile.TemporaryDirectory() as temp_dir:
                    output_path = Path(temp_dir) / "solution.lean"
                    await project.get_solution(output_path=output_path)
                    if output_path.exists():
                        solution_content = output_path.read_text()
                        data["solution"] = solution_content
                        
                        if save_solution_to:
                            save_path = Path(save_solution_to)
                            save_path.write_text(solution_content)
                            data["saved_to"] = str(save_path.absolute())
            except Exception as e:
                data["solution_error"] = str(e)
                
        return json.dumps(data, indent=2)
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 mentions that it 'Returns full project data including solution if available,' which hints at read-only behavior and output content, but lacks details on permissions, error handling, rate limits, or whether it's idempotent. For a tool with no annotation coverage, this is insufficient.

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 extremely concise with two sentences that directly state the tool's function and output. Every word earns its place, and it's front-loaded with the core purpose, making it efficient and well-structured.

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 (2 parameters, no annotations, but has an output schema), the description is minimally adequate. The output schema likely covers return values, so the description doesn't need to detail them, but it lacks context on usage, parameters, and behavioral traits, leaving gaps in completeness.

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?

The description doesn't explain the parameters beyond what the schema provides. With 0% schema description coverage, it fails to compensate by adding meaning to 'project_id' or 'save_solution_to.' However, since there are only 2 parameters and one is optional with a default, the baseline is slightly above minimal, but it doesn't enhance 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 tool's purpose with a specific verb ('Checks') and resource ('Aristotle project'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'list_recent_projects' or the various 'prove_' tools, which would require a 5.

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. It doesn't mention when to choose 'get_project_status' over 'list_recent_projects' or any of the 'prove_' tools, nor does it specify prerequisites or exclusions for usage.

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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