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project_create

Create a new project within an organization by specifying name, organization ID, repository path, and description to establish project structure.

Instructions

PROJECT MANAGEMENT: Create a new project under an organization.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
org_idYesOrganization ID (case-insensitive)
nameYesProject name
repo_pathNoPath to git repo
descriptionNoProject description

Implementation Reference

  • Handler logic for the 'project_create' tool: parses arguments into ProjectCreate model, calls db.create_project, and returns JSON response.
    if name == "project_create":
        project = db.create_project(
            ProjectCreate(
                org_id=args["org_id"],
                name=args["name"],
                repo_path=args.get("repo_path"),
                description=args.get("description"),
            )
        )
        return f"Created project: {_json(project)}"
  • Registration of the 'project_create' tool in the MCP server's list_tools() handler, including input schema definition.
    Tool(
        name="project_create",
        description="PROJECT MANAGEMENT: Create a new project under an organization.",
        inputSchema={
            "type": "object",
            "properties": {
                "org_id": {"type": "string", "description": "Organization ID (case-insensitive)"},
                "name": {"type": "string", "description": "Project name"},
                "repo_path": {"type": "string", "description": "Path to git repo"},
                "description": {"type": "string", "description": "Project description"},
            },
            "required": ["org_id", "name"],
        },
    ),
  • Pydantic model defining the input schema for creating a project (org_id, name, optional repo_path and description).
    class ProjectCreate(BaseModel):
        org_id: str
        name: str
        repo_path: str | None = None
        description: str | None = None
  • Database helper method that inserts a new project record into the SQLite database using the provided ProjectCreate data.
    def create_project(self, data: ProjectCreate) -> Project:
        id = self._gen_id()
        now = self._now()
        normalized_org_id = self._normalize_id(data.org_id)
        # Check if a case-insensitive match already exists for org_id
        existing_org = self.conn.execute(
            "SELECT id FROM orgs WHERE LOWER(id) = ?", (normalized_org_id,)
        ).fetchone()
        if existing_org:
            org_id = existing_org["id"]  # Use existing org ID
        else:
            org_id = normalized_org_id  # Use normalized org_id for new entries
        self.conn.execute(
            """INSERT INTO projects (id, org_id, name, repo_path, description, created_at)
               VALUES (?, ?, ?, ?, ?, ?)""",
            (id, org_id, data.name, data.repo_path, data.description, now),
        )
        self.conn.commit()
        return Project(
            id=id,
            org_id=org_id,
            name=data.name,
            repo_path=data.repo_path,
            description=data.description,
            created_at=datetime.fromisoformat(now),
        )
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 of behavioral disclosure. It states the tool creates a project, implying a write/mutation operation, but fails to describe permissions needed, whether creation is idempotent, error handling, or what the response includes (e.g., project ID). This is a significant gap for a mutation tool with zero annotation coverage.

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 a single, efficient sentence with zero waste. It is front-loaded with the key action and context, making it easy to parse quickly without unnecessary details.

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

Completeness2/5

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

Given the complexity of a creation tool with no annotations and no output schema, the description is incomplete. It lacks behavioral details (e.g., permissions, response format) and usage guidelines, which are critical for an AI agent to invoke this tool correctly in a real-world context.

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?

Schema description coverage is 100%, so the schema already documents all four parameters (org_id, name, repo_path, description) and their required status. The description adds no additional meaning beyond implying 'org_id' is needed for context, which is redundant with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('Create a new project') and the resource ('under an organization'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'project_list' or 'org_create', which would require mentioning what makes this tool unique for project creation.

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 does not mention prerequisites (e.g., needing an existing organization), exclusions, or comparisons to siblings like 'org_create' or 'project_list', leaving the agent to infer usage 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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