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create_project

Submit a real-world project by specifying location, requirements, and budget. The system decomposes work into tasks for human workers.

Instructions

Submit a new project to RobotFail.

Describe what you need done in the physical world. Be specific about location, requirements, and deliverables. The PM engine decomposes it into atomic tasks and assigns them to human workers.

Args: description: What you need done. Include location, requirements, deliverables. budget_dollars: Budget in USD (e.g. 50.00 = $50). Minimum $5.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYes
budget_dollarsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The tool handler function for 'create_project' — it posts a project description and budget to the RobotFail API and returns the JSON response.
    @mcp.tool()
    async def create_project(description: str, budget_dollars: float) -> str:
        """Submit a new project to RobotFail.
    
        Describe what you need done in the physical world. Be specific about
        location, requirements, and deliverables. The PM engine decomposes it
        into atomic tasks and assigns them to human workers.
    
        Args:
            description: What you need done. Include location, requirements, deliverables.
            budget_dollars: Budget in USD (e.g. 50.00 = $50). Minimum $5.
        """
        data = await _post("/api/projects", {
            "description": description,
            "budget": budget_dollars,
        })
        return json.dumps(data, indent=2)
  • The @mcp.tool() decorator registers 'create_project' as a tool with the FastMCP server instance, making it available via the MCP interface.
    @mcp.tool()
  • The _post helper function used by create_project to make the HTTP POST request to the RobotFail API.
    async def _post(path: str, body: dict = None) -> dict:
        async with httpx.AsyncClient(timeout=60) as client:
            r = await client.post(f"{API_BASE}{path}", headers=_headers(), json=body or {})
            r.raise_for_status()
            return r.json()
  • Input parameters for create_project: description (str) and budget_dollars (float).
    async def create_project(description: str, budget_dollars: float) -> str:
        """Submit a new project to RobotFail.
    
        Describe what you need done in the physical world. Be specific about
        location, requirements, and deliverables. The PM engine decomposes it
        into atomic tasks and assigns them to human workers.
    
        Args:
            description: What you need done. Include location, requirements, deliverables.
            budget_dollars: Budget in USD (e.g. 50.00 = $50). Minimum $5.
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool triggers a multi-step process (PM engine decomposes and assigns to workers) and sets a minimum budget ($5). This adds value beyond the tool's name. However, it lacks details on error handling or side effects.

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 concise with 4 sentences plus an args section. Information is front-loaded with purpose and usage, and every sentence adds value. No redundancy or fluff.

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 (2 params, existence of output schema), the description is complete. It explains the process, parameter constraints, and usage context. The output schema likely covers return values, so no need to repeat.

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

Parameters5/5

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

Schema coverage is 0%, so the description fully compensates by explaining both parameters: description ('include location, requirements, deliverables') and budget_dollars ('Budget in USD, minimum $5'). This adds significant meaning beyond the bare schema types.

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 tool's purpose with a specific verb+resource ('Submit a new project to RobotFail') and explains the process (decomposition into tasks by PM engine). It distinguishes itself from sibling tools like approve_project or list_projects by focusing on creation.

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

Usage Guidelines4/5

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

The description gives good context on when to use: 'when you need something done in the physical world' and specifies to include location, requirements, and deliverables. However, it does not explicitly state when not to use or mention alternatives, which would improve clarity.

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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