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Positronikal

DaVinci MCP Professional

by Positronikal

list_projects

Retrieve all projects in the current DaVinci Resolve database, enabling AI assistants to display available projects for selection and further processing.

Instructions

List all available projects in the current database

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function that executes the 'list_projects' tool logic. Calls GetProjectListInCurrentFolder() on the DaVinci Resolve project manager, filtering out empty strings.
    def list_projects(self) -> list[str]:
        """List all projects in the current database."""
        self._ensure_connected()
    
        if self._project_manager:
            projects = self._project_manager.GetProjectListInCurrentFolder()
            return [p for p in projects if p]  # Filter out empty strings
        return []
  • Schema/Protocol definition for DaVinciProjectManager, which defines GetProjectListInCurrentFolder() used by list_projects.
    @runtime_checkable
    class DaVinciProjectManager(Protocol):
        """Protocol for DaVinci Resolve ProjectManager objects."""
    
        def GetCurrentProject(self) -> DaVinciProject | None:
            """Get the currently open project."""
            ...
    
        def GetProjectsInDatabase(self) -> list[dict[str, Any]]:
            """Get all projects in the current database."""
            ...
    
        def GetProjectListInCurrentFolder(self) -> list[str]:
            """Get project list in current folder."""
            ...
  • Registration of the 'list_projects' tool in the MCP tools registry, defining its name, description, and empty input schema.
    # Project tools
    types.Tool(
        name="list_projects",
        description="List all available projects in the current database",
        inputSchema={"type": "object", "properties": {}, "required": []},
    ),
  • Dispatch handler in the server that routes 'list_projects' calls to resolve_client.list_projects(). Also referenced in the resource handler for 'resolve://projects' URI.
    async def _call_tool(self, name: str, arguments: dict[str, Any]) -> Any:
        """Dispatch a tool call to the resolve client."""
        if name == "get_version":
            return self.resolve_client.get_version()
        elif name == "get_current_page":
            return self.resolve_client.get_current_page()
        elif name == "switch_page":
            return self.resolve_client.switch_page(arguments.get("page", ""))
        elif name == "list_projects":
            return self.resolve_client.list_projects()
  • Resource handler that also uses list_projects() for the 'resolve://projects' resource URI.
    async def _read_resource(self, uri: str) -> Any:
        """Dispatch a resource read to the resolve client."""
        if uri == "resolve://version":
            return self.resolve_client.get_version()
        elif uri == "resolve://current-page":
            return self.resolve_client.get_current_page()
        elif uri == "resolve://projects":
            return self.resolve_client.list_projects()
Behavior3/5

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

Without annotations, the description implies a read-only operation but does not explicitly state it or mention any side effects, pagination, or response details.

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?

Single sentence, no unnecessary words, clearly conveys the function.

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?

Adequate for a parameterless list tool, but could describe the output format or mention that it returns all projects without filtering.

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?

Input schema has no parameters (baseline 4), and description appropriately conveys no need for parameters.

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 action (list), resource (projects), and scope (all available in current database). It distinguishes from siblings like 'create_project' and 'get_current_project'.

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?

No guidance on when to use this tool versus alternatives like 'get_current_project' for a specific project or when not to use it.

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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