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Microsoft Fabric MCP Server

by snahrup

get_powerbi_datasets

Retrieve all Power BI datasets from a Microsoft Fabric workspace to access and manage data models for analysis and reporting.

Instructions

Get all Power BI datasets in the workspace

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual implementation of fetching datasets from Power BI.
    async getDatasets(): Promise<Dataset[]> {
      try {
        // Use workspace-specific endpoint if workspace ID is available
        const workspaceId = process.env.POWERBI_WORKSPACE_ID;
        const endpoint = workspaceId 
          ? `/groups/${workspaceId}/datasets`
          : '/datasets';
        
        const response = await this.apiClient.get(endpoint);
        return response.data.value;
      } catch (error) {
        console.error('Error fetching datasets:', error);
        throw error;
      }
    }
  • src/index.ts:58-64 (registration)
    Tool registration for get_powerbi_datasets.
      name: 'get_powerbi_datasets',
      description: 'Get all Power BI datasets in the workspace',
      inputSchema: {
        type: 'object',
        properties: {},
      },
    },
  • The handler in index.ts that maps the MCP request to the powerBIClient implementation.
    case 'get_powerbi_datasets': {
      const datasets = await powerBIClient.getDatasets();
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(datasets, null, 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 states the tool retrieves datasets but doesn't mention critical details like whether it's a read-only operation, if it requires authentication, potential rate limits, or what the return format looks like (e.g., list, pagination). This leaves significant gaps for a tool interacting with a data service like Power BI.

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, clear sentence that front-loads the essential information ('Get all Power BI datasets in the workspace') with zero waste. It's appropriately sized for a simple tool with no parameters, making it easy for an agent to parse quickly.

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 Power BI operations and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'datasets' entail, how results are returned (e.g., JSON structure, error handling), or prerequisites like workspace access. For a tool in a data analytics context, more detail is needed to ensure correct usage.

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?

The input schema has 0 parameters with 100% coverage, so there's no need for parameter documentation in the description. The description appropriately doesn't discuss parameters, which is efficient and avoids redundancy. A baseline of 4 is applied since no parameters exist, and the description doesn't add unnecessary information.

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 ('Get') and resource ('all Power BI datasets in the workspace'), making the tool's purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'get_workspaces' or 'refresh_dataset', which would require more specificity for a perfect score.

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 like 'get_workspaces' (which might list workspaces rather than datasets) or 'refresh_dataset' (which modifies datasets). It lacks explicit when-to-use or when-not-to-use instructions, leaving the agent to infer context from tool names alone.

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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