Skip to main content
Glama

get_project_list

Retrieve all projects from the Vibe system. Optionally filter by environment to scope the list.

Instructions

Retrieve all projects from the Vibe system

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
environmentNoDevelopment

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The _execute_get_project_list method is the core handler that executes the get_project_list tool logic. It extracts the 'environment' parameter, calls vibe_client.get_project_list(), and returns a JSON-formatted TextContent result.
    async def _execute_get_project_list(
        self,
        tool_input: dict[str, Any],
        log: structlog.stdlib.BoundLogger,
    ) -> list[TextContent]:
        """Execute get_project_list tool."""
        environment = tool_input.get("environment", "Development")
    
        response = await self.vibe_client.get_project_list(environment=environment)
    
        result = {
            "status": "success",
            "data": response.model_dump(),
            "project_count": len(response.projects),
        }
    
        log.info("Tool executed successfully", result=result)
        return [TextContent(type="text", text=json.dumps(result, indent=2))]
  • The VibeAPIClient.get_project_list method is the service-level handler that performs the actual API call to GET /api/Project/GetProjectList, validates the request using GetProjectListRequest, normalizes the response, and returns a GetProjectListResponse.
    async def get_project_list(
        self,
        environment: str = "Development",
    ) -> GetProjectListResponse:
        """
        Get list of all projects.
    
        Args:
            environment: Environment name (production, development, etc.)
    
        Returns:
            GetProjectListResponse with list of projects
    
        Raises:
            MCPToolExecutionError: If API call fails
        """
        log = self.logger.bind(
            method="get_project_list",
            environment=environment,
        )
    
        try:
            # Create request object (validates input)
            request = GetProjectListRequest(environment=environment)
            log.debug("Fetching project list", request=request.model_dump())
    
            # Make API call
            response_data = await self.http_client.get(
                endpoint=API_ENDPOINTS["GET_PROJECT_LIST"],
                params={"environment": request.environment},
            )
    
            # Validate and transform response. The Vibe API currently returns a
            # bare project array, while older docs showed a wrapped object.
            response = GetProjectListResponse(
                **self._normalize_project_list_response(response_data)
            )
            log.debug(
                "Project list retrieved successfully",
                project_count=len(response.projects),
            )
    
            return response
    
        except Exception as e:
            log.error("Failed to get project list", error=str(e))
            raise MCPToolExecutionError(
                f"Failed to retrieve project list: {str(e)}"
            ) from e
  • The @mcp.tool decorator registers 'get_project_list' as a FastMCP tool with name='get_project_list' and description='Retrieve all projects from the Vibe system'. The wrapper function extracts the 'environment' parameter and delegates to VibeMCPTools._execute_get_project_list.
    @mcp.tool(
        name="get_project_list",
        description="Retrieve all projects from the Vibe system",
    )
    async def get_project_list(environment: str = "Development") -> str:
        try:
            result = await _tools()._execute_get_project_list(
                {"environment": environment},
                logger.bind(tool="get_project_list"),
            )
            return result[0].text
        except Exception as e:
            log = logger.bind(tool="get_project_list")
            log.error("Tool execution failed", error=str(e))
            raise MCPToolExecutionError(f"Tool execution failed: {str(e)}") from e
  • GetProjectListRequest: Pydantic model for input validation with an 'environment' field (default='Development').
    class GetProjectListRequest(BaseModel):
        """Request model for GetProjectList endpoint."""
    
        model_config = ConfigDict(extra="forbid")
    
        environment: str = Field(
            default="Development",
            description="Environment (production, development, etc.)",
        )
  • GetProjectListResponse: Pydantic model for response validation with 'success' (bool) and 'projects' (list of ProjectDto) fields.
    class GetProjectListResponse(BaseModel):
        """Response model for GetProjectList endpoint."""
    
        model_config = ConfigDict(extra="allow")
    
        success: bool = Field(description="Success indicator")
        projects: list[ProjectDto] = Field(
            default_factory=list,
            description="List of projects",
        )
Behavior2/5

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

With no annotations, the description fails to disclose critical behavioral traits like pagination, authentication requirements, or whether the operation is safe. It only states 'retrieve,' implying read-only, but no depth is provided.

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 a single, brief sentence that clearly states its purpose. It is front-loaded and efficient, though it could benefit from slight expansion without losing conciseness.

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?

Despite having an output schema, the description lacks context about what the output contains, how many projects are returned, or the effect of the 'environment' parameter. It feels incomplete for a list endpoint.

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

Parameters1/5

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

The input schema has 1 parameter ('environment') with 0% description coverage. The description does not mention this parameter nor clarify its purpose or effect, adding no value beyond the schema.

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 explicitly states 'Retrieve all projects from the Vibe system,' specifying a clear verb ('retrieve') and resource ('projects from Vibe system'). This distinguishes the tool from siblings like 'create_bucket' or 'get_code'.

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 is provided on when to use this tool versus alternatives, such as when to use 'get_project_list' vs 'get_activity' or 'get_code'. No criteria for selection are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Coding-Professional/McpServer_Vibe'

If you have feedback or need assistance with the MCP directory API, please join our Discord server