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Dataverse MCP Server

by mwhesse

Get Dataverse Table

get_dataverse_table

Retrieve detailed metadata and configuration information for a specific Dataverse table to inspect its structure and properties.

Instructions

Retrieves detailed information about a specific Dataverse table including its metadata, properties, and configuration. Use this to inspect table definitions and understand table structure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
logicalNameYesLogical name of the table to retrieve

Implementation Reference

  • Handler function that retrieves detailed metadata for a Dataverse table by its logical name using the DataverseClient's getMetadata method.
    async (params) => {
      try {
        const result = await client.getMetadata<EntityMetadata>(
          `EntityDefinitions(LogicalName='${params.logicalName}')`
        );
    
        return {
          content: [
            {
              type: "text",
              text: `Table information for '${params.logicalName}':\n\n${JSON.stringify(result, null, 2)}`
            }
          ]
        };
      } catch (error) {
        return {
          content: [
            {
              type: "text",
              text: `Error retrieving table: ${error instanceof Error ? error.message : 'Unknown error'}`
            }
          ],
          isError: true
        };
      }
    }
  • Input schema and metadata (title, description) for the get_dataverse_table tool, requiring a 'logicalName' string.
    {
      title: "Get Dataverse Table",
      description: "Retrieves detailed information about a specific Dataverse table including its metadata, properties, and configuration. Use this to inspect table definitions and understand table structure.",
      inputSchema: {
        logicalName: z.string().describe("Logical name of the table to retrieve")
      }
    },
  • Registers the 'get_dataverse_table' tool on the MCP server within the getTableTool function.
    server.registerTool(
      "get_dataverse_table",
      {
        title: "Get Dataverse Table",
        description: "Retrieves detailed information about a specific Dataverse table including its metadata, properties, and configuration. Use this to inspect table definitions and understand table structure.",
        inputSchema: {
          logicalName: z.string().describe("Logical name of the table to retrieve")
        }
      },
      async (params) => {
        try {
          const result = await client.getMetadata<EntityMetadata>(
            `EntityDefinitions(LogicalName='${params.logicalName}')`
          );
    
          return {
            content: [
              {
                type: "text",
                text: `Table information for '${params.logicalName}':\n\n${JSON.stringify(result, null, 2)}`
              }
            ]
          };
        } catch (error) {
          return {
            content: [
              {
                type: "text",
                text: `Error retrieving table: ${error instanceof Error ? error.message : 'Unknown error'}`
              }
            ],
            isError: true
          };
        }
      }
    );
  • src/index.ts:140-140 (registration)
    Top-level call to getTableTool which registers the get_dataverse_table tool on the main MCP server instance.
    getTableTool(server, dataverseClient);
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. It states 'retrieves' and 'inspect,' implying a read-only operation, but doesn't disclose behavioral traits such as authentication requirements, rate limits, error handling, or what happens if the table doesn't exist. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 two sentences, front-loaded with the core purpose and followed by usage guidance. It's efficient with minimal waste, though it could be slightly more structured by separating purpose and guidelines more clearly. Every sentence adds value, earning a high score for conciseness.

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

Completeness3/5

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

Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and basic usage but lacks behavioral details like error handling or return format. For a read operation with no output schema, more context on what 'detailed information' includes would improve completeness.

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%, with the single parameter 'logicalName' documented as 'Logical name of the table to retrieve.' The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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 tool's purpose: 'Retrieves detailed information about a specific Dataverse table including its metadata, properties, and configuration.' It specifies the verb 'retrieves' and resource 'Dataverse table' with scope 'detailed information.' However, it doesn't explicitly distinguish from siblings like 'list_dataverse_tables' or 'get_dataverse_column,' which would require a 5.

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

Usage Guidelines3/5

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

The description includes 'Use this to inspect table definitions and understand table structure,' which implies usage context for inspecting specific tables. However, it lacks explicit guidance on when to use this versus alternatives like 'list_dataverse_tables' for listing tables or 'get_dataverse_column' for column details, and no exclusions or prerequisites are mentioned.

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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